The study used open-access Human Connectome Project data from participants aged 22 to 36 and older. After researchers excluded 21 people with missing analysis variables, the final sample included 743 adults, representing 97.3% retention of the eligible group. Magnetic resonance images had been acquired on a 3 Tesla Siemens Skyra scanner, and the cortex was reconstructed with FreeSurfer 5.3.0-HCP. Using the Desikan–Killiany atlas, the researchers extracted thickness and surface-area measurements from 22 bilateral prefrontal regions, including lateral frontal, orbitofrontal and anterior cingulate areas. The analysis was cross-sectional, so it could identify associations but not determine whether brain structure influences behavior, behavior influences brain structure, or both reflect shared genetic or environmental influences.

Rather than examining each brain region separately, the researchers used principal component analysis to identify recurring patterns in which prefrontal measurements covaried across people. This structural covariance approach describes how morphology varies together in a population; a person’s component score represents how strongly that individual expresses a group-wide pattern, not the strength of that person’s direct anatomical connections. Before the analysis, age, sex and intracranial volume were removed from the regional measurements. An oblique rotation was then used because neighboring prefrontal regions may share anatomical features and can legitimately covary. The investigators also examined whether the component solutions remained stable when the sample was repeatedly resampled.

The retained cortical-thickness solution contained three components and accounted for 53.7% of the variance. The largest, RC1, grouped the superior and middle frontal regions with the pars opercularis and was labeled global or lateral prefrontal thickness. RC2 combined the lateral and medial orbitofrontal cortex with the pars orbitalis, while RC3 was centered on the anterior and posterior portions of the anterior cingulate cortex. This three-part anatomy broadly resembled established lateral, orbital and medial divisions of the prefrontal cortex, supporting the study’s first hypothesis only partly. Parallel analysis actually favored a two-component solution, so the three-component arrangement was retained because it had been specified in advance. Bootstrapping also revealed uneven stability: congruence fell below 0.85 in 19.4% of resamples for RC1 and 28.0% for RC2, compared with 0.7% for RC3.

The thickness analysis initially produced two associations with cognition. People expressing more of the lateral-prefrontal thickness pattern RC1 obtained slightly lower scores on the NIH Toolbox Flanker test of inhibitory control and attention: the standardized coefficient was β = −0.103, with p = 0.027 and partial eta-squared of 0.007. The direction was opposite to the prediction that greater lateral-prefrontal expression would accompany better performance, and the result did not survive false discovery rate correction across the four directional hypothesis tests, where q = 0.054. It also had a straightforward alternative: RC1 correlated strongly with whole-cortex mean thickness, at r = 0.85. Mean thickness alone predicted lower Flanker scores, and after global thickness was regressed out of the regional data before repeating the principal component analysis, the RC1 association was no longer significant. The evidence therefore favored a global rather than specifically prefrontal relationship, although these sensitivity analyses were added after review and were not prespecified.

A second thickness result concerned the Gambling RT Difference, calculated as the median time to make a “larger” guess in reward blocks minus the corresponding time in punishment blocks. Because block context was not cued before each guess, the measure was interpreted as the carryover of recent outcomes into response speed, not as a direct measure of whether participants preferred larger rewards to larger punishments. Greater expression of the orbitofrontal thickness component RC2 was associated with a smaller difference, with β = −0.103, p = 0.025 and partial eta-squared of 0.007. Like the Flanker result, it failed false discovery rate correction at q = 0.054 across the prespecified family. It also disappeared under robust regression, after influential cases were removed, and when global mean thickness was regressed out before the component analysis. The corresponding relationship for “smaller” guesses pointed in the same direction but was not significant, so the data did not establish a reliable orbitofrontal effect.

The most notable behavioral result came from a separate analysis of surface area, which produced a three-component solution accounting for 39.9% of the variance. Its topology only partly matched the thickness solution: the largest Tucker’s congruence between corresponding components was 0.81, indicating that the two measures captured related but distinguishable patterns. The orbitofrontal surface-area component AC2 was associated with faster 2-Back reaction time, the speed of correctly processing working-memory information presented two trials earlier. The standardized coefficient was β = −0.125, p = 0.003 and partial eta-squared of 0.012. It survived false discovery rate correction across 12 surface-area component tests, where q = 0.032, but not across all 24 thickness and surface-area component tests, where q = 0.064. The association was not predicted in the original hypotheses and was therefore exploratory.

Most robustness checks supported the AC2 association. It remained significant with heteroskedasticity-consistent standard errors, robust regression, removal of influential cases, and adjustment for intracranial volume. It also was not a simple speed–accuracy tradeoff: greater AC2 expression was associated with higher 2-Back accuracy, and including accuracy as a covariate reduced but did not eliminate the reaction-time relationship. Yet the regional specificity of the finding remained uncertain. AC2 correlated with total cortical surface area at r = 0.38, and total surface area alone was associated with faster 2-Back reaction time before adjustment for intracranial volume. When total surface area was regressed out of the regional measurements before repeating the principal component analysis, the reconstructed AC2 closely resembled the original component, with Tucker’s congruence of 0.91, but its reaction-time coefficient was cut roughly in half to β = −0.065 and became non-significant at p = 0.078. The result could therefore not be cleanly separated from global brain size.

Several limitations constrain the interpretation. The HCP sample includes monozygotic and dizygotic twins and non-twin siblings, but the open-access release did not contain family identifiers, so related participants could not be modeled with mixed-effects analyses. Treating them as independent likely makes the reported p and q values anti-conservative. The sample also excluded education data available only in the restricted release, limiting adjustment for a factor known to influence cognitive performance. Some behavioral measures had limited reliability or could not test their intended hypotheses: for example, the block-level gambling choice score lacked the trial-level information needed to assess win-stay and lose-switch behavior. In addition, FreeSurfer thickness is an indirect morphometric measure influenced by myelination, neuronal structure, partial-volume effects and surface reconstruction, while the atlas groups some cytoarchitecturally distinct areas into coarse parcels. All behavioral models explained no more than 2.7% of variance, and the magnitude of the reported effects was small by conventional standards. Replication with independent samples, explicit family-structure modeling, education adjustment, finer anatomical parcellation and complementary diffusion or myelin imaging would be needed before the findings could be treated as evidence of distinct structure–function relationships in the prefrontal cortex.

Subject of Research: Exploratory analysis of prefrontal cortical morphometric covariance and cognitive performance in healthy young adults

Article Title: Prefrontal cortical morphometric covariance and cognitive task performance in healthy young adults: an exploratory analysis of Human Connectome Project data

Article References: Chivers, R. (2026). Prefrontal cortical morphometric covariance and cognitive task performance in healthy young adults: an exploratory analysis of Human Connectome Project data. Frontiers in Human Neuroscience, 20, Article 1976371. https://doi.org/10.3389/fnhum.2026.1976371

Image Credits: AI Generated

DOI: 10.3389/fnhum.2026.1976371

Keywords: Human Connectome Project, prefrontal cortex, structural MRI, cortical thickness, brain surface area, working memory, inhibitory control, orbitofrontal cortex, morphometric covariance, cognitive neuroscience, Prefrontal, cortical

The study, published in BMC Psychiatry by Jianbo Zhang, Shuo Yang and colleagues at the Second Affiliated Hospital of Anhui Medical University and partner institutions, used a technique called voxel-based morphometry, or VBM, to compare gray matter volume changes across the whole brain. VBM works by normalizing each patient’s structural MRI scan to a standard brain template, segmenting the image into gray matter, white matter and cerebrospinal fluid, and then statistically comparing the density of gray matter at every voxel, the tiny three-dimensional pixels that make up the scan. By running this analysis on scans taken before abstinence and again after one month without alcohol, the team could pinpoint exactly where brain structure shifted during the dry interval.

The results read like a map of recovery in motion. Compared with pre-abstinence scans, patients showed increased gray matter volume in the right Rolandic operculum, middle cingulate cortex, middle temporal gyrus, caudate nucleus, precuneus, superior temporal gyrus, middle occipital gyrus and the pulvinar of the thalamus. On the left side of the brain, volume rose in the insular cortex, superior temporal gyrus, cuneus, gyrus rectus, middle temporal gyrus and inferior temporal gyrus. These are not random addresses. The insula is central to interoception, the brain’s monitoring of internal bodily states, and it is heavily implicated in craving and withdrawal. The cingulate cortex governs conflict monitoring and emotional control. The caudate nucleus is a key node in the reward circuitry that alcohol hijacks. The temporal lobes support memory and auditory processing, while the precuneus anchors self-referential thought.

Clinically, the patients improved as well. The team administered the Montreal Cognitive Assessment, or MoCA, alongside the Self-Rating Anxiety Scale and the Self-Rating Depression Scale, and found that cognitive function improved while anxiety and depression scores fell following abstinence. In an exploratory analysis that was not corrected for multiple comparisons, the researchers observed an association between gray matter changes in the right caudate nucleus and MoCA scores, with a correlation coefficient of 0.3841 and a P value of 0.0329. That association did not survive false discovery rate correction, with a corrected q value of 0.4710, so the authors treat it as a preliminary clue rather than a confirmed finding. Still, the direction of the result, linking caudate structural change to cognitive improvement, fits with the caudate’s role in learning and reward-based cognition.

The most technically ambitious part of the study came next. Rather than stopping at the observation that gray matter had changed, the researchers asked whether the spatial pattern of those changes bore any relationship to the brain’s molecular architecture. To do this, they turned to normative positron emission tomography-derived maps of neurotransmitter receptor and transporter density, publicly available atlases that describe where in the healthy human brain particular receptors are most concentrated. Using Spearman’s rank correlation, a statistical method that tests whether two spatial patterns rise and fall together across the brain, the team compared the whole-brain map of gray matter volume changes against the spatial distributions of six neurotransmitter systems: serotonin, dopamine, GABA, norepinephrine, acetylcholine and glutamate.

Three systems stood out with remarkable strength. Gray matter volume changes were significantly negatively correlated with the spatial distribution of serotonin 2A receptors, with a rho of -0.7557 and a P value below 0.001 that survived false discovery rate correction at q = 0.0075. The correlation with GABA-A receptors was even stronger, at rho = -0.8290, also surviving correction. Two independent mGluR5 reference maps, labeled abp_hc28_dubois and abp_hc73_smart, produced rho values of -0.6395 and -0.6757 respectively, both highly significant and both surviving correction. A negative spatial correlation means that gray matter volume increased most in regions where these receptors are least densely packed, and least in regions where the receptors are most concentrated. In other words, the recovery pattern is not uniform; it is systematically related to the molecular identity of each region.

Why would that be? The authors suggest the finding provides preliminary clues for understanding the pathophysiological mechanisms underlying alcohol abstinence-related brain alterations by revealing spatial relationships between gray matter changes and neurotransmitter-related molecular architecture. Serotonin 2A receptors are involved in mood regulation, cognition and perceptual processing, and chronic alcohol exposure disrupts serotonergic signaling. GABA-A receptors are the principal inhibitory receptors in the brain, and alcohol acts directly on GABA-A function, which is why withdrawal is characterized by hyperexcitability as the inhibitory system recalibrates. Metabotropic glutamate receptor 5, or mGluR5, modulates excitatory neurotransmission and has been linked to reward seeking and relapse vulnerability in preclinical and clinical addiction research. The fact that gray matter recovery patterns align with all three systems, each confirmed with two independent mGluR5 maps, suggests that the molecular environment of each brain region shapes how quickly and how strongly its structure responds to abstinence.

It is worth pausing on what a negative correlation with GABA-A receptors might mean mechanistically. Regions with the highest GABA-A density, such as parts of the cortex and limbic system, are precisely where alcohol’s inhibitory effects are strongest during drinking. When alcohol is removed, those regions undergo the most dramatic neurochemical upheaval during withdrawal, and the new study’s data suggest their structural response during the first month is comparatively muted. Conversely, regions with lower GABA-A density show the largest gray matter gains. One interpretation is that the most alcohol-saturated molecular territories need more time to reorganize, while less saturated regions respond more quickly. The authors are careful not to overclaim; the study is exploratory in parts and the spatial correlations, while statistically robust, do not establish causation between receptor density and structural change.

The study has limitations that the team acknowledges implicitly through its design. Forty patients is a modest sample, the follow-up interval is short at one month, and the clinical association with MoCA scores did not survive multiple comparison correction. The neurotransmitter maps are normative, derived from healthy brains rather than from the patients themselves, so the correlations describe spatial overlap rather than direct measurement of the patients’ receptor systems. Longitudinal studies with larger cohorts, longer abstinence intervals and patient-specific molecular imaging would be needed to confirm whether the receptor-structure relationship holds over time and whether it predicts relapse or sustained recovery.

Even with those caveats, the study represents a meaningful step in addiction neuroscience. It moves beyond the question of whether the brain changes during abstinence to ask how those changes are organized, and it answers with a molecular vocabulary. The finding that gray matter volume increases in the insula, temporal lobe, middle cingulate gyrus and caudate nucleus after just one month of abstinence, and that the spatial pattern of those increases tracks with serotonin 2A, GABA-A and mGluR5 distributions, provides a framework for thinking about recovery as a receptor-guided process. If future work confirms and extends these relationships, the neurotransmitter atlas could become a predictive tool, helping clinicians anticipate which brain regions will respond to abstinence and which molecular systems might be targeted to support structural recovery in patients with alcohol use disorder.

Subject of Research: Neuroscience

Article Title: Gray matter volume alterations following short-term abstinence in alcohol use disorder and their spatial association with neurotransmitter distributions

Article References: Zhang, J., Yang, S., Chen, Z., Zhang, D., & Yao, W. (2026). Gray matter volume alterations following short-term abstinence in alcohol use disorder and their spatial association with neurotransmitter distributions. BMC Psychiatry. https://doi.org/10.1186/s12888-026-08768-6

Image Credits: AI Generated

DOI: 10.1186/s12888-026-08768-6

Keywords: alcohol use disorder, gray matter volume, short-term abstinence, voxel-based morphometry, neurotransmitter atlas, serotonin 2A receptors, GABA-A receptors, mGluR5, structural MRI, BMC Psychiatry, Gray, matter

The methodological heart of the study lies in its commitment to data-driven phenotyping. Rather than assuming in advance which symptom clusters matter, the researchers applied K-means clustering to 174 individual clinical items drawn from validated instruments, including the Patient Health Questionnaire-9, the Generalized Anxiety Disorder-7, the Young Mania Rating Scale, the Hypomania Checklist-32, the Snaith-Hamilton Pleasure Scale, the Insomnia Severity Index, the Dysfunctional Attitudes Scale, and measures of non-suicidal self-injury and psychotic symptoms rated on the Brief Psychiatric Rating Scale. Clustering at the level of individual questionnaire items, rather than summed scale scores, allowed the algorithm to detect combinations of symptoms that cut across conventional diagnostic boundaries. The procedure produced two stable, non-overlapping transdiagnostic phenotypes, and the cluster solution held up under internal validation using the adjusted Rand index.

The first group was labeled the Negative Affect-Anhedonic phenotype. Its defining features were self-injurious tendencies together with depressive and anhedonic symptoms — the withdrawal, joylessness, and despair that most people associate with severe depression, but which in this sample appeared in patients carrying either diagnosis. The second group, the Manic-Activation phenotype, was characterized by manic and hypomanic activation: elevated mood, heightened drive, reduced need for sleep, and accelerated thinking and behavior. Crucially, each phenotype drew members from both diagnostic camps. A patient with a bipolar diagnosis could belong to the negative affect cluster, and a patient diagnosed with unipolar depression could show prominent manic-activation features. The researchers report that phenotype dominance only partially aligned with categorical diagnosis, revealing substantial cross-diagnostic overlap and equally substantial heterogeneity within each diagnosis.

With the symptom phenotypes established, the team turned to the brain. All participants underwent high-resolution 3 Tesla structural magnetic resonance imaging, and the scans were processed with FreeSurfer, the widely used reconstruction pipeline, to extract three complementary measures of neuroanatomy: cortical thickness, cortical surface area, and subcortical volumes. These metrics are biologically distinct. Cortical thickness reflects the width of the folded gray matter ribbon and is thought to relate to dendritic arborization and synaptic density, whereas surface area reflects the degree of cortical folding and the expansion of functional columns; the two show largely independent genetic determinants. Subcortical volumes capture structures such as the hippocampus, amygdala, and basal ganglia, regions repeatedly implicated in mood regulation. To relate these anatomical measures to the symptom phenotypes, the researchers used sparse canonical correlation analysis, a multivariate technique that identifies weighted patterns of variables on both sides of the equation that maximally covary with one another — an approach well suited to the complexity of brain-wide morphology paired with multi-item symptom profiles.

The results delivered a clear modality split. Both the Negative Affect-Anhedonic and Manic-Activation phenotypes showed statistically significant multivariate associations with cortical thickness and with cortical surface area, but neither was linked to subcortical volumes in the full sample. Statistical significance was established through permutation testing, and the stability of individual features was verified with bootstrapping, giving the team confidence that the brain-symptom relationships were not artifacts of a single analytic pass. The absence of subcortical findings is itself notable: despite decades of attention to the hippocampus and amygdala in mood disorders, the strongest transdiagnostic signals in this cohort lived in the cortex.

Perhaps the most striking technical finding concerns the spatial organization of the two cortical metrics. The associations involving cortical thickness were focal, concentrated in higher-order association regions — the areas of the cortex that integrate information across sensory modalities and support complex cognition, self-referential processing, and emotion regulation. By contrast, the associations involving cortical surface area were spatially distributed across much of the cortical sheet but appeared to be network-organized, suggesting coordinated maturational or structural variation along the lines of large-scale neural networks rather than isolated regional differences. This thickness-versus-area dissociation matters because the two metrics index different cellular and developmental processes, and the study’s authors interpret the pattern as evidence that transdiagnostic symptom phenotypes map onto partially dissociable, modality-specific structural brain signatures rather than a single undifferentiated anomaly.

The study also tackled a developmental question. Because the sample spanned adolescence and emerging adulthood — a window during which the brain undergoes dramatic cortical thinning and network refinement — the researchers tested whether the strength of brain-symptom associations varied continuously with age in 960 participants aged 10 to 29 years. Six models were examined, crossing each symptom phenotype with each structural modality. The verdict after correction for multiple comparisons was uniformly negative: no age-by-symptom interaction survived false discovery rate correction across the six tested models. The smallest nominal effect appeared in the Negative Affect-Anhedonic model with subcortical volumes, but the authors were explicit that this effect failed to survive correction and should be treated as exploratory only. In a field where developmental moderation claims are often made on nominal p-values, this restrained reporting is a methodological lesson in itself.

The clinical implications are layered. At a practical level, the findings lend empirical weight to dimensional frameworks such as the Research Domain Criteria initiative advanced by the United States National Institute of Mental Health, which encourages researchers and clinicians to study psychopathology along measurable dimensions rather than relying solely on DSM-5 categories. The Wuhan team’s data show that symptom-based phenotypes capture clinically meaningful heterogeneity that categorical diagnoses obscure — two patients with the same diagnostic label can sit in opposite symptom clusters, and two patients with different labels can share a cluster and, potentially, a common cortical profile. That said, the researchers caution that the phenotypes only partially aligned with diagnosis, so the categorical system has not been rendered obsolete; rather, it appears to be an incomplete lens. A multimetric framework that tracks both cortical thickness and surface area, alongside dimensional symptom measures, may better capture the shared and distinct biology of major mood disorders.

There are also important limits to keep in view. The study is cross-sectional, so it can demonstrate that symptom phenotypes and cortical anatomy covary, but not that one causes the other — cortical differences could predispose to particular symptom patterns, chronic illness could reshape the cortex, medications could contribute, or a third factor could drive both. The clustering solution, while internally stable, represents one data-driven cut through a vast symptom space, and alternative algorithms or item sets might yield different groupings. And the null developmental findings mean only that no moderation was detectable in this cohort with these methods, not that age is irrelevant to brain-symptom relationships. The study was registered prospectively at the Chinese Clinical Trial Registry (ChiCTR2500106094), received ethics approval from Renmin Hospital of Wuhan University, and was funded by the National Key R&D Program of China and the National Natural Science Foundation of China, among other sources.

Even with those caveats, the scale and rigor of the work make it a landmark in the effort to connect the messy, overlapping reality of mood disorder symptoms to measurable brain structure. By showing that data-derived symptom phenotypes — not diagnostic labels — align with reproducible, modality-specific patterns of cortical morphology in more than a thousand patients, the study offers a template for how large clinical cohorts, item-level machine learning, and multivariate neuroimaging statistics can be combined to move psychiatry beyond its century-old categories. The next step, the researchers and their colleagues in the field will likely agree, is longitudinal: following patients over time to see whether these cortical signatures predict symptom trajectories, treatment response, or the emergence of manic episodes in those who today carry only a depression diagnosis.

Subject of Research: Neuroanatomical correlates of transdiagnostic symptom phenotypes in major depressive and bipolar disorders

Article Title: Neuroanatomical correlates of transdiagnostic clinical phenotypes in major depressive and bipolar disorders

Article References: Lu, X., Hu, M., Luo, D., Hou, H., Feng, M., He, J., Wang, W., Yang, B. X., & Zong, X. (2026). Neuroanatomical correlates of transdiagnostic clinical phenotypes in major depressive and bipolar disorders. BMC Psychiatry. https://doi.org/10.1186/s12888-026-08602-z

Image Credits: AI Generated

DOI: 10.1186/s12888-026-08602-z

Keywords: major depressive disorder, bipolar disorder, transdiagnostic psychiatry, structural MRI, cortical thickness, cortical surface area, K-means clustering, sparse canonical correlation analysis, FreeSurfer, Research Domain Criteria, anhedonia, mania

The research team drew on two of the largest repositories in American dementia research: the National Alzheimer’s Coordinating Center, which aggregates standardized clinical evaluations from 40 federally funded Alzheimer’s Disease Research Centers, and NIAGADS, the National Institute on Aging’s genetics storage service. From these databases they assembled a cohort of 1,129 participants with known family history, with a mean age of 69 years, 61 percent of whom were women. Forty-four percent reported a mother or maternal relative with dementia, mild cognitive impairment, or Alzheimer’s disease, while the remainder reported either only paternal history or no family history at all, with both parents living past age 60 and cognitively normal. About 71 percent of participants had normal cognition at the time of imaging, and roughly 12 percent met criteria for dementia.

The predictive features came from two biologically motivated sources. The first was structural magnetic resonance imaging, processed with the automated segmentation tool FreeSurfer, yielding nine carefully chosen measures: the hippocampal occupancy score, which reflects shrinkage of the hippocampus relative to the inferior lateral ventricle; white matter hyperintensity volume, a marker of cerebrovascular damage; and normalized volumes of seven cortical and subcortical regions, including the precuneus, entorhinal cortex, lingual gyrus, inferior and superior temporal gyri, inferior parietal cortex, and fusiform gyrus. The second source was mitochondrial DNA, the small circular genome inherited almost exclusively through the maternal line. Using HaploGrep 2 software, the researchers classified each participant’s mitochondrial haplogroup, an inherited branch on the maternal phylogenetic tree that reflects ancient ancestry and can influence mitochondrial function.

The machine learning pipeline was rigorous. The data were split 80:20 into training and testing sets using stratified sampling to preserve the proportion of family-history-positive cases, and hyperparameters were tuned with grid search combined with four-fold cross-validation. Seven algorithms competed: decision tree, logistic regression, naive Bayes, linear and radial support vector machines, linear discriminant analysis, and XGBoost, a gradient-boosted ensemble method that builds many decision trees sequentially and corrects each one’s errors. Models were compared on accuracy and the area under the receiver operating characteristic curve, and feature importance was assessed through a rank-based integration of two complementary metrics: how frequently XGBoost used each feature across its tree ensemble, and the discriminative range of each feature’s conditional probabilities in the naive Bayes model.

The results were modest but statistically meaningful. Among imaging-only models, XGBoost performed best, reaching an accuracy and AUC of 0.59, with sensitivity of about 0.56 and specificity of 0.62; naive Bayes followed closely at 0.58. A label-shuffling permutation test, in which outcome labels were randomly permuted 1,000 times to generate a null distribution, confirmed that the XGBoost model performed significantly better than chance, with an empirical p-value of 0.006. The standout feature was the volume of the precuneus, a large region on the inner surface of the parietal lobes, followed by the hippocampal occupancy score, entorhinal cortex, lingual gyrus, and temporal regions. The least informative features were the inferior parietal cortex, fusiform gyrus, and white matter hyperintensity volume.

The precuneus is a compelling winner. It serves as a central hub of the brain’s default mode network, the constellation of regions most active during rest and introspection, and it is among the earliest and most heavily affected areas in Alzheimer’s disease. Previous work by the same group and others has shown that cognitively normal people with a maternal family history display altered gray matter volume, progressive atrophy in the precuneus and medial temporal cortex over two years, abnormal default mode network activity centered on this region, and elevated amyloid-beta deposition specifically in the precuneus and posterior parietal and cingulate cortices. Postmortem studies add a mechanistic thread: reduced activity of the choline acetyltransferase enzyme in the precuneus has been linked to increased amyloid binding, higher soluble amyloid-beta 42 levels, and more advanced Alzheimer’s pathology.

The mitochondrial story is more nuanced. When the researchers added haplogroup classifications to the imaging data, the sample shrank by roughly 36 percent to 717 participants, because not everyone had genetic data available. Smaller samples usually degrade predictive performance, yet the combined model held its ground: XGBoost achieved an accuracy and AUC of 0.62, with sensitivity of 0.61 and specificity of 0.63. The R0 intermediate clade emerged as the most influential mitochondrial feature, though the precuneus remained the single most important variable overall. The authors are candid that the added value of the mitochondrial data remains unclear, since feature importance indicated minimal contribution relative to imaging. Still, the fact that performance did not collapse despite the much smaller sample suggests the haplogroups carry some signal worth pursuing.

The mechanistic rationale for looking at mitochondria at all is rooted in maternal inheritance. Because mitochondrial DNA passes almost exclusively from mother to child, it offers a natural explanation for why maternal, but not paternal, family history elevates amyloid accumulation independently of APOE epsilon 4 status, the strongest common genetic risk factor. Earlier studies found reduced cytochrome oxidase activity in adult children of mothers with Alzheimer’s disease but not fathers, and postmortem analyses have linked lower mitochondrial genome copy numbers in the brain to increased odds of Alzheimer’s neuropathology. Notably, the predictive models deliberately excluded APOE epsilon 4 status to avoid problems with incorporating ordinal variables, and when the team statistically adjusted for age, sex, race, ethnicity, and APOE genotype before modeling, performance dropped for most algorithms, indicating that some of the discriminative signal reflects demographic and genetic differences between the family-history groups rather than a purely imaging-based signature.

The authors are appropriately cautious about clinical translation. Repeated analyses across ten random seeds yielded mean AUCs near 0.53, revealing limited stability, and restricting the sample to self-reported white participants reduced performance further. The cohort was overwhelmingly of European ancestry, limiting generalizability to populations with different haplogroup distributions, and family history relied on self-report, which is vulnerable to recall bias. The cross-sectional design cannot establish whether precuneus differences precede or follow other disease changes. The researchers emphasize that these findings should not be used for individual risk prediction, and they flag the ethical concerns of communicating probabilistic susceptibility without definitive interventions. What the study does deliver is a framework: proof of concept that multimodal data can partially recover maternal family history when it is unknown, a reinforcement of the precuneus as the signature region of maternally inherited risk, and a roadmap toward larger, longitudinal, ancestrally diverse studies that incorporate rare mitochondrial variants and copy number. If that promise is fulfilled, the invisible half of a family tree may one day be read from a scan and a blood sample.

Subject of Research: Machine learning prediction of maternal family history of Alzheimer's disease using structural neuroimaging and mitochondrial haplogroups

Article Title: Using machine learning of neuroimaging and mitochondrial haplogroups to predict maternal family history of Alzheimer’s disease

Article References: Veatch, O. J., Dutta, S., Mansel, C. O., Townley, R., Sardiu, M. E., & Honea, R. A. (2026). Using machine learning of neuroimaging and mitochondrial haplogroups to predict maternal family history of Alzheimer’s disease. PLOS Aging and Health, 1(3), e0000045. https://doi.org/10.1371/journal.page.0000045

Image Credits: AI Generated

DOI: 10.1371/journal.page.0000045

Keywords: Alzheimer's disease, maternal family history, machine learning, XGBoost, precuneus, mitochondrial DNA, haplogroups, structural MRI, neuroimaging, APOE epsilon 4, default mode network, risk prediction

The research team, led by Robin Kamrla and Konrad Schöniger of Martin Luther University Halle-Wittenberg, analyzed structural MRI scans and questionnaire data from 261 healthy adults aged 18 to 64. Participants completed the German version of the Freiburg Mindfulness Inventory, a 14-item questionnaire that yields separate scores for presence and acceptance, alongside clinical interviews and standardized measures of depression and anxiety. All participants were free of current psychiatric diagnoses and psychotropic medication, allowing the researchers to examine how everyday differences in trait mindfulness map onto brain structure in a non-clinical population.

What set this study apart was its analytical strategy. Rather than correlating each mindfulness subscale with brain structure separately — the approach taken by most previous work — the team built a single multiple regression model that included both subscales simultaneously and then explicitly tested for regions where the two moved in opposite directions. This differential dissociation approach directly asks whether a brain region grows with one component while shrinking with the other, a pattern that would be invisible, or even misleadingly averaged away, in conventional univariate analyses. Grey matter volume was estimated with voxel-based morphometry using high-resolution T1-weighted images acquired on a 3 Tesla scanner, and results were corrected for multiple comparisons using non-parametric threshold-free cluster enhancement with 5,000 permutations.

The results revealed a striking split. Acceptance, measured by items such as being friendly to oneself when things go wrong, correlated positively with grey matter volume in the left rolandic operculum and the left postcentral gyrus — regions that make up the primary and secondary somatosensory cortex, the brain’s machinery for perceiving bodily and sensory signals. Presence, by contrast, was negatively associated with grey matter in those same cortical areas. In other words, the more a participant scored on acceptance relative to presence, the larger the volume of their somatosensory regions; the more they scored on presence relative to acceptance, the smaller those volumes appeared.

The second direction of dissociation produced an equally compelling finding, this time in the limbic system. Within a targeted region-of-interest analysis of the amygdala, the almond-shaped structure central to emotion perception and threat processing, grey matter volume in the right hemisphere was negatively correlated with acceptance and positively correlated with presence. Higher presence paired with lower acceptance was associated with a larger amygdala, while higher acceptance with lower presence accompanied a smaller one. This pattern echoes the Monitor and Acceptance Theory of mindfulness proposed by Lindsay and Creswell in 2017, which holds that monitoring — the presence-like component — can intensify all experiences, negative, positive, and neutral alike, whereas acceptance dampens affective reactivity and counterbalances that amplification.

The psychometric data reinforced the neural story. Acceptance scores correlated strongly and negatively with both depression and anxiety symptoms, and these relationships held even after statistically controlling for presence. Presence, meanwhile, showed correlations with depression and anxiety that vanished entirely once acceptance was taken into account. This mirrors the earlier work of Kohls and colleagues, who found that only acceptance, not presence, was linked to reduced anxiety and depression — the very conditions for which mindfulness-based interventions appear most effective. The new study extends that dissociation from questionnaires to brain anatomy, suggesting that the clinical benefits of mindfulness may flow primarily through acceptance-related neural circuits.

Post-hoc analyses sharpened the picture further. When each subscale was examined separately while controlling for the other, acceptance was associated with grey matter in the bilateral rolandic operculum, bilateral postcentral gyrus, supplementary motor area, cingulate cortex, and precuneus, as well as limbic structures including the right amygdala, parahippocampal gyrus, and superior temporal pole. Presence, in its unique variance, was linked to negative associations in somatosensory and auditory regions such as Heschl’s gyrus. The researchers interpret this as evidence that acceptance is tied to the deep, non-judgmental processing of bodily sensations and emotions, whereas presence reflects the capacity for intentional shifts of attention — a refocusing skill that, on its own, does not engage the emotion-integration circuitry.

The authors are careful about what these structural correlations can and cannot mean. Grey matter volume is an indirect marker, mediated by factors such as neural efficiency and plasticity, and the study’s cross-sectional design cannot determine whether brain shape shapes mindfulness or mindfulness practice reshapes the brain. The sample was also predominantly young and healthy, limiting generalizability to clinical populations, and individual meditation experience was not assessed. The researchers themselves note that the study was not preregistered and that their post-hoc analyses captured only the variance unique to each subscale, not the substantial portion the two share — the subscales correlated at r = 0.56 in this sample.

Even with those caveats, the implications are considerable. If mindfulness is treated as a single unified construct, interventions may inadvertently train the wrong component for the wrong goal. Someone high in presence but low in acceptance might, according to the Monitor and Acceptance Theory, experience heightened affective reactivity — attending intensely to everything without the emotional integration that acceptance provides. The new neurostructural findings give that theoretical framework an anatomical footing: presence was associated only with neocortical regions, while acceptance reached into the limbic system in the opposite polarity. The two subdomains, the authors argue, may function as complementary and partially compensatory mechanisms within the broader mindfulness construct.

Future work, the team suggests, should test whether the same dissociation appears in functional MRI responses to emotional stimuli, and whether longitudinal or intervention-based designs can establish causal relationships between meditation practice, acceptance, and limbic structure. The differential analysis framework itself may prove portable, offering a template for teasing apart compensatory mechanisms in other psychological constructs that have been lumped together under single labels. For now, the message for both scientists and practitioners is clear: the mindful brain is not one thing. Being fully present and being fully accepting recruit different, and in places opposing, neural signatures — and understanding that duality may be the key to explaining why mindfulness helps some minds and, occasionally, intensifies the experience of others.

Subject of Research: Neurostructural correlates of the presence and acceptance subdomains of trait mindfulness in healthy adults

Article Title: Neural Correlates of Dissociating Mindfulness Subdomains: A Differential Analysis Approach

Article References: Kamrla, R., Schöniger, K., König, P., Zwiky, E., Küttner, A., Scheiwe, G. S., Andreev, J., Fleischer, L., Uhlig, M., Ptasczynski, L. E., Spitzer, M., Deistung, A., Wohlgemuth, W. A., Selle, J., & Redlich, R. (2026). Neural Correlates of Dissociating Mindfulness Subdomains: A Differential Analysis Approach. Mindfulness. https://doi.org/10.1007/s12671-026-02993-6

Image Credits: AI Generated

DOI: 10.1007/s12671-026-02993-6

Keywords: mindfulness, presence, acceptance, grey matter volume, amygdala, voxel-based morphometry, Freiburg Mindfulness Inventory, Monitor and Acceptance Theory, somatosensory cortex, emotion regulation, structural MRI, trait mindfulness

Mild cognitive impairment, often abbreviated as MCI, is one of the most diagnostically frustrating states in medicine. It describes a genuine decline in memory or thinking that goes beyond normal aging, yet it sits on a fault line: some people with MCI stabilize or even revert to normal cognition, while others progress to full dementia, most commonly Alzheimer’s disease. Previous meta-analyses of dozens of cohort studies have shown that annual conversion rates vary enormously between clinical settings, and a large fraction of people diagnosed with MCI never develop dementia at all. This heterogeneity is precisely why clinicians have long sought reliable tools to separate the two trajectories, and why misclassification carries such high stakes, either exposing stable patients to unnecessary treatment or delaying care for those truly at risk.

The research team, led by Bipul Simkhada of Florida International University together with colleagues at the University of Miami, the University of Florida, and Clemson University, tackled the problem with a multimodal machine-learning framework. They assembled 2008 samples drawn from 828 unique individuals with MCI in the Alzheimer’s Disease Neuroimaging Initiative, one of the largest and most thoroughly characterized longitudinal dementia datasets in existence. Each sample combined three distinct families of information: cognitive and functional assessments gathered during clinical visits, demographic and genetic variables including APOE genotype, and structural features extracted from magnetic resonance imaging of the brain. Based on what actually happened over a two-year follow-up window, subjects were labeled as either stable MCI, comprising 1306 samples, or progressive MCI, comprising 702 samples that converted to dementia.

Methodological rigor was central to the design. Rather than relying on a single train-test split, which can inflate performance estimates, the researchers used nested cross-validation, a scheme in which model selection and performance evaluation are separated at every step. They also addressed the inherent class imbalance between stable and progressive samples using the synthetic minority over-sampling technique, known as SMOTE, and paid careful attention to feature scaling, calibration of predicted probabilities, and the selection of clinically meaningful decision thresholds. These details matter because a prediction tool that reports probabilities poorly calibrated against real-world outcomes can mislead physicians even when its raw accuracy appears impressive. The team’s final model was a calibrated version of XGBoost, a gradient-boosted decision tree algorithm widely used in biomedical prediction for its ability to capture nonlinear interactions between variables without requiring enormous computing resources.

The results were striking. During nested cross-validation, the multimodal XGBoost model achieved a balanced accuracy of 81.28 percent, with a standard deviation of 2.56 percent, meaning that it correctly identified both converters and non-converters at nearly equal rates despite the unequal group sizes. On an independent held-out test set that the model had never seen during training, balanced accuracy reached 81.99 percent, with a bootstrap mean of 81.97 percent and a 95 percent confidence interval spanning 78.22 to 85.51 percent. Crucially, the multimodal combination outperformed any single data type used alone. Cognitive tests, demographic risk factors, and brain imaging each carried predictive signal, but their fusion proved strictly superior, underscoring a complementary relationship between the observable decline in day-to-day function and the silent anatomical erosion happening inside the brain.

What elevates the study beyond a simple accuracy benchmark is its insistence on explainability. Black-box predictions have historically been a barrier to clinical adoption, so the researchers interrogated their model using SHAP, or SHapley Additive exPlanations, a technique derived from cooperative game theory that assigns each input feature a precise contribution to every individual prediction, alongside permutation feature importance, which measures how much performance degrades when a given variable is shuffled. The analyses converged on a coherent and clinically intuitive picture. The strongest predictors included functional impairment measures, specifically the Functional Activities Questionnaire and the Clinical Dementia Rating Sum of Boxes, which capture whether a person is struggling with finances, medications, shopping, and other instrumental tasks of daily living.

Cognitive performance scores followed closely, particularly the 11-item and 13-item versions of the Alzheimer’s Disease Assessment Scale cognitive subscale and the Mental State Examination, familiar bedside instruments that quantify memory, orientation, language, and attention. Alongside these behavioral markers, the model leaned heavily on biological signals: the APOE ε4 allele, the best-established genetic risk factor for sporadic Alzheimer’s disease, and a constellation of structural brain abnormalities. The imaging features that mattered most mapped onto regions long implicated in the disease, including the hippocampus, the memory-forming structure that atrophies earliest, the lateral ventricles that expand as surrounding tissue shrinks, the parietal and temporal lobes where cortical thinning tracks disease spread, and the amygdala, a deep structure involved in emotion and memory circuits.

The convergence of these strands is perhaps the study’s most important conceptual message. Neither cognitive test scores nor genetic risk nor imaging atrophy alone tells the whole story, but when a patient shows functional decline on questionnaires, poor performance on structured cognitive batteries, carries an ε4 allele, and exhibits measurable shrinkage in exactly the brain regions vulnerable to Alzheimer’s pathology, the probability of near-term conversion rises sharply. The researchers describe this alignment of cognitive-functional impairment and region-specific neurodegeneration as the strongest indicator of impending dementia, a finding that reinforces the biological intuition that clinical symptoms and structural damage advance together during the transition period.

The practical implications are considerable. Because all of the input data can be collected during a single clinical visit, without longitudinal repeated scans or experimental fluid biomarkers, the framework could in principle be deployed in memory clinics using instruments that clinicians already administer. An accurate two-year risk estimate would allow physicians to triage patients, directing high-risk individuals toward early anti-amyloid therapy, closer monitoring, and participation in clinical trials, while sparing lower-risk patients unnecessary interventions and anxiety. It would also sharpen the design of prevention studies, which currently spend enormous resources enrolling participants who never progress. The team has made its analysis code publicly available on GitHub, and the underlying data come from the openly accessible ADNI database, allowing other groups to validate and extend the approach.

Challenges remain before such models enter routine practice. ADNI participants are volunteers who tend to be well educated and predominantly of European ancestry, and recent systematic reviews have flagged limited generalizability and high risk of bias across many published MCI prediction models, so independent validation in diverse, real-world populations will be essential. Still, the combination of robust validation methodology, calibrated probabilities, explainable outputs, and accuracy above 80 percent using only routine clinical data marks a meaningful advance. As the population ages and the number of people living with mild cognitive impairment climbs into the tens of millions worldwide, tools that can distinguish, at the first appointment, who will decline and who will not, may fundamentally reshape how medicine confronts the earliest and most treatable stage of dementia.

Subject of Research: Multimodal machine-learning prediction of two-year conversion from mild cognitive impairment to dementia

Article Title: Multimodal prediction of MCI-to-dementia conversion over a two-year window

Article References: Simkhada, B., Liang, T. Y., Cui, X., Adeyosoye, M., Simkhada, B., Cabrerizo, M., Cid, R. C., Burke, S. L., Barreto, A., Rishe, N., Loewenstein, D. A., & Adjouadi, M. (2026). Multimodal prediction of MCI-to-dementia conversion over a two-year window. GeroScience. https://doi.org/10.1007/s11357-026-02551-x

Image Credits: AI Generated

DOI: 10.1007/s11357-026-02551-x

Keywords: mild cognitive impairment, Alzheimer's disease, dementia, machine learning, XGBoost, explainable AI, SHAP, structural MRI, APOE ε4, hippocampal atrophy, ADNI, GeroScience

The study, led by researchers using an unusually sensitive combination of brain imaging techniques, set out to answer a deceptively simple question. If structural plasticity in the form of new synapse formation underlies learning-related gray matter changes, then a rigorous motor learning intervention should produce detectable shifts in both measures over the same time window. Prior animal studies had shown that learning new motor skills, such as reaching tasks in rats or acrobatic training in mice, leads to synaptogenesis in the motor cortex within days to weeks. If the same biology holds in humans, the argument went, modern imaging should be able to catch it in the act.

To test this, the team recruited a small cohort of healthy adult participants and put them through a carefully controlled motor learning protocol. The task was designed to be challenging enough to drive genuine learning, with performance improvements tracked session by session to confirm that participants were actually acquiring the skill rather than simply going through the motions. Crucially, the researchers combined positron emission tomography with a synaptic vesicle glycoprotein ligand, a radiotracer that binds to proteins found abundantly in the presynaptic terminals of neurons. This class of tracer, sometimes described as offering a molecular window into synaptic density, is among the most direct non-invasive measures of synapse abundance available in living humans.

In parallel, the participants underwent high-resolution structural magnetic resonance imaging, allowing the researchers to quantify gray matter volume in regions implicated in motor learning, including the primary motor cortex, the supplementary motor area, the cerebellum, and the striatum. The logic of the design was elegant in its redundancy. If learning leaves structural fingerprints, those fingerprints should appear in the tracer signal, in the anatomical volumes, or in both, and any changes should correlate with how well individuals learned the task.

When the data came in, the story that emerged was one of stability rather than transformation. Participants improved substantially on the motor task, confirming that the intervention was behaviorally effective. Yet comparisons of tracer binding and gray matter volumes before and after the training period revealed no statistically significant changes in any of the regions examined. Individual differences in the amount of learning did not predict changes in synaptic density measures, and there was no evidence of the kind of localized volume increases reported in some earlier motor learning studies.

The finding does not mean that nothing changed in the brains of the learners. The authors are careful to point out several plausible explanations for the null result, each of which carries important implications for how the field interprets structural plasticity research. One possibility is that the synaptic changes accompanying motor learning are either too small in magnitude or too spatially diffuse to be detected by current imaging technology, even with the sensitivity of modern synaptic tracers. Synaptogenesis in animal models tends to involve a modest net increase in synapse number, and the fraction of synapses that turn over in a given cortical region may be a tiny proportion of the total pool that the tracer signal samples.

Another possibility concerns timing and scale. Animal work shows that synapse formation can be highly transient, with newly formed spines appearing and disappearing over days, and a substantial fraction being pruned away shortly after training ends. If human motor learning follows a similar trajectory, the window between the end of training and the post-training scan could have missed a transient surge in synaptic remodeling. Gray matter volume changes, meanwhile, may reflect processes other than synaptogenesis altogether, such as changes in dendritic spines, glial cells, vasculature, or interstitial fluid, meaning that volume and synaptic density are not simply two views of the same underlying biology.

The pilot nature of the study also deserves honest scrutiny. The sample size was small, as is typical for studies combining PET imaging with repeated structural MRI, and statistical power to detect subtle within-subject changes is limited when cohort numbers run low. The authors frame the work explicitly as exploratory, designed to establish feasibility and generate effect size estimates that can inform larger, better-powered follow-up studies. Null results in small samples are notoriously ambiguous; they may reflect genuine stability, or they may reflect an inability to detect changes that a larger cohort would reveal. Both interpretations remain on the table.

Still, the study arrives at a moment when claims about rapid structural plasticity in the adult human brain have become a staple of popular science coverage and, increasingly, of clinical optimism. Exercise interventions, cognitive training programs, and rehabilitation protocols are often marketed with the promise that they can rebuild the brain, implicitly or explicitly invoking the gray matter gains reported in landmark learning studies. If those gains prove difficult to replicate with state-of-the-art measures of synaptic architecture, the field may need to recalibrate both its expectations and its explanatory language. The relationship between macroscopic volume changes and microscopic synaptic remodeling may be far looser than the standard narrative suggests.

What the study ultimately offers is a dose of methodological rigor applied to one of neuroscience’s most appealing ideas. The learning brain is undoubtedly changing, as decades of electrophysiology, animal work, and human imaging attest. But the assumption that such change must be legible in every measurable index of brain structure, on the timescale of a typical training study, is a hypothesis rather than a fact. By subjecting that hypothesis to a direct test with two complementary imaging modalities, and by publishing a transparent null result, the researchers have done the field a service that positive findings rarely provide. The next generation of plasticity studies will be better designed, better powered, and more appropriately cautious precisely because studies like this one have mapped the limits of what current tools can see.

Subject of Research: Synaptic density and gray matter volume changes following motor learning in healthy adults

Article Title: No significant changes in synaptic density and gray matter volume following motor learning—a pilot study

Article References: Hehl, M., Toyonaga, T., Carson, R. E., Dupont, P., Van Laere, K., Swinnen, S. P., & Cuypers, K. (2026). No significant changes in synaptic density and gray matter volume following motor learning—a pilot study. npj Science of Learning. https://doi.org/10.1038/s41539-026-00451-5

Image Credits: AI Generated

DOI: 10.1038/s41539-026-00451-5

Keywords: motor learning, synaptic density, gray matter volume, brain plasticity, PET imaging, synaptic vesicle glycoprotein, structural MRI, motor cortex, cerebellum, neuroplasticity, null result, pilot study