The toolkit, developed by Allegra Benzini, Alyssa M. Lanzi, Cinzia Palmirotta, Simona Aresta, Petronilla Battista, and Gaia C. Santi of the Istituti Clinici Scientifici Maugeri IRCCS and the University of Delaware, extends the CHAT transcription conventions and the CLAN analysis software developed within the CHILDES/TalkBank framework to Italian-speaking adults with neurological disorders. CHAT—Codes for the Human Analysis of Transcripts—and its companion program CLAN have long been the backbone of quantitative language analysis, implemented in 28 languages and applied to a database of more than 44 million spoken words. Yet the system was originally designed around English child language, and its conventions presuppose English grammar, English phonology, and English clinical presentation. The Italian adaptation, presented as a downloadable operational manual, systematically reshapes those conventions to fit the structure of Italian and the realities of aphasic speech.

The methodological heart of the work is a transparent decision tree applied to every symbol in the CHAT manual. Each of the 59 transcription symbols was evaluated symbol by symbol against explicit inclusion and exclusion criteria: symbols had to be relevant to adult clinical populations and to linguistic phenomena associated with neurologically based language disorders such as post-stroke aphasia, mild cognitive impairment, primary progressive aphasia, Parkinson’s disease, and Alzheimer’s disease. Symbols dedicated to child language development or to conversational analysis notation irrelevant to adult neurological speech were excluded from consideration from the start. The outcome of that evaluation was decisive: 40 symbols were retained unchanged, 15 were removed, four were adapted for Italian, and one was newly created. A parallel process addressed the 11 error-coding symbols, of which six were retained, four were adapted, and one new marker was introduced, with no symbols eliminated outright.

Many of the removals follow directly from the architecture of Italian. As a syllable-timed language, Italian does not systematically encode secondary stress at the lexical level, so the secondary stress marker was dropped. Vowel lengthening, common in Italian and heavily influenced by non-pathological dialectal variation, would confound clinical interpretation, so the lengthened-syllable marker went as well. Punctuation symbols such as the comma, semicolon, and colon were excluded because they neither define units of analysis nor influence automated CLAN outputs such as mean utterance length. The multiple-repetition marker was deemed redundant, already captured by the existing repetition symbol, and the unclear retracing notation was removed because it served only conversion to a competing transcription system. These are small deletions, but together they strip the transcription of artifacts that would otherwise inject noise into quantitative measures of Italian speech.

The adaptations cut deeper. In English, the omission of subjects, auxiliaries, or function words is typically a grammatical violation, so the original CHAT system codes such omissions with an error marker. Italian, however, is a null-subject language: dropping the pronoun in a phrase like “ha gli occhiali da sole”—literally “has the sunglasses”—is perfectly grammatical. The Italian toolkit therefore restricts the omitted-word marker to contexts where omission genuinely violates Italian grammar, and limits the grammatical error marker to utterances whose well-formedness fails by Italian, not English, standards. The clause delimiter was likewise repurposed to tag subordinate clauses, giving clinicians a direct metric of syntactic complexity that the English system computes automatically but that had no Italian equivalent—critical for characterizing the telegraphic, agrammatic output of many aphasic patients.

Phonological error coding underwent a deliberate simplification. The extensive English taxonomy of phonological error subtypes was collapsed into two operational categories: one for recognizable phonological errors such as omissions, substitutions, insertions, and metatheses, and one for phonological neologisms—complete losses of the intended target that may co-occur with non-word transcription symbols. Morphological coding was similarly tailored to the Italian inflectional system, split into markers for agreement errors of gender, number, and person, and for errors of verbal morphology including tense, mood, and irregular inflections. Because Italian verbs carry their grammatical freight in rich inflectional endings rather than in separate auxiliary words, morphological errors reveal a different facet of aphasia in Italian than they do in English.

Two entirely new symbols fill gaps that became glaring in clinical practice. The first, a word fragment marker, captures partial word productions that are abandoned without reformulation and without a precisely recognizable lexical target—a phenomenon the authors note is frequent in adult neurological speech and central to the lexical retrieval difficulties that constitute the most common aphasic disorder. The second, an articulatory distortion marker, codes phonetic distortions in which sounds are mangled despite correct phonological sequencing, a hallmark of motor speech impairments such as apraxia of speech and dysarthria. The original English system could represent such behaviors structurally, but could not quantify them at the error-coding level, even though distinguishing dysarthria from apraxia of speech from phonological difficulty is essential for differential diagnosis in the clinic.

To demonstrate the toolkit’s discriminative power, the researchers applied it to a connected speech sample from a 64-year-old right-handed woman with the non-fluent variant of primary progressive aphasia, a condition defined by effortful, agrammatic, often barely intelligible speech. Her sample, elicited with the picture-description task of the Screening for Aphasia in Neurodegeneration, was transcribed in Italian with the adapted conventions and compared with its English translation transcribed under the original CHAT rules. The side-by-side comparison exposes how the same clinical behavior is encoded differently depending on the language of analysis: the Italian transcript flags articulatory distortions, repeated phonological fragments, and retracing sequences through the new markers, while the English transcript must insert a null subject and assign a grammatical error code to subject omission that is simply licensed grammar in Italian. Compound nouns provide a further illustration, since English compounds rely on hierarchical stress patterns that Italian lexical items, carrying a single primary stress, do not.

Beyond the immediate benefit to Italian clinicians and researchers, the study stakes out a broader claim about how speech analysis should travel across languages. The authors emphasize that the majority of the world’s population does not speak English, yet research and clinical tools remain overwhelmingly Anglophone, and directly transferring English instruments often fails for diagnosis, treatment, and cross-cultural comparison. Their prior cross-linguistic work comparing healthy speakers of English, Italian, Cantonese, and Mandarin found significant differences in function word use and phonology that reflect language-specific morphosyntactic rules, reinforcing the point that linguistic diversity shapes the very manifestation of language processing patterns. The Italian toolkit therefore aims at a balance: preserving the conceptual organization and operational principles of TalkBank so that data remain comparable across languages, while introducing targeted adjustments grounded in the structure of Italian. It is designed as a flexible resource in which users select coding options guided by specific clinical or research goals, whether at the word or sentence level, or across the phonetic–phonological, lexical–semantic, morphosyntactic, and pragmatic domains.

The authors are candid about limitations. The adaptation targets adult speakers with neurological disorders and does not address developmental populations, where further modifications would be needed; coding remains time-consuming and may constrain large-scale implementation; and the morphosyntactic annotation layer—the %mor and %pos tiers—was deliberately left untouched, since adapting it requires alignment with both Italian grammar and specific research hypotheses. Future work, they suggest, should explore tighter integration between standardized coding schemes and automated transcription approaches such as Whisper-based speech recognition. Still, the toolkit stands as a replicable model for extending standardized annotation systems to any structurally distinct language, a necessary step if the rapidly advancing field of speech-based neurological biomarkers is to serve patients in the languages they actually speak.

Subject of Research: Cross-linguistic adaptation of standardized speech transcription and coding tools for assessing neurological language disorders in Italian-speaking adults.

Article Title: Extending standardized speech analysis across languages: An Italian toolkit for transcription and coding of neurological speech

Article References: Benzini, A., Lanzi, A. M., Palmirotta, C., Aresta, S., Battista, P., & Santi, G. C. (2026). Extending standardized speech analysis across languages: An Italian toolkit for transcription and coding of neurological speech. Behavior Research Methods, 58(10), Article 292. https://doi.org/10.3758/s13428-026-03175-x

Image Credits: AI Generated

DOI: 10.3758/s13428-026-03175-x

Keywords: speech analysis, CHAT, CLAN, TalkBank, aphasia, primary progressive aphasia, cross-linguistic adaptation, Italian language, neurodegenerative disease, connected speech, language annotation, behavior research methods

Frontotemporal dementia, or FTD, belongs to a spectrum of disorders called frontotemporal lobar degeneration, which primarily strikes the frontal and temporal lobes of the brain. Unlike Alzheimer’s disease, which typically announces itself through memory lapses, FTD often begins with changes in behavior, personality, and social cognition. The behavioral variant of the disease, known as bvFTD, is particularly notorious for producing stereotyped, compulsive, and ritualistic behaviors, along with a striking vulnerability to addiction-related conduct such as excessive alcohol and tobacco use. Primary progressive aphasia, which includes the nonfluent and semantic variants, erodes language abilities while leaving other functions relatively intact for longer periods.

Researchers at the Center for Neurodegenerative Diseases and the Aging Brain at the University of Bari Aldo Moro, working with the Pia Fondazione Card. Panico Hospital in Tricase, Italy, set out to determine whether patients with FTD are prone to problematic internet and social media use. Between January 2023 and December 2024, they retrospectively screened consecutive patients diagnosed with mild cognitive impairment or Alzheimer’s disease, behavioral variant FTD, and primary progressive aphasia. Diagnoses followed established consensus criteria, and patients with documented histories of overuse, compulsivity, or dependency on digital technologies were identified through clinical records and caregiver interviews.

To characterize these behaviors rigorously, the team administered a semi-structured interview grounded in the biopsychosocial model of addiction developed by Griffiths, probing six core components of behavioral addiction: salience, mood modification, tolerance, withdrawal symptoms, conflict, and relapse. Overuse was operationally defined as quantitatively excessive time online, often exceeding twelve hours per day, that disrupted daily routines. Compulsivity captured repetitive, ritualistic digital behaviors lacking any functional goal, such as constant nonfunctional scrolling or repetitive video streaming. Dependency required the presence of the six-component addiction profile. Because patients with FTD frequently lack insight into their own behavior, interviews were conducted separately with patients and caregivers.

The results were striking. Of 61 patients evaluated in the FTD group, 14, or 22.9 percent, exhibited problematic internet or social media use. Ten of these had behavioral variant FTD and four had primary progressive aphasia, with three carrying pathogenic genetic variants, two in C9orf72 and one in GRN. By contrast, in a control cohort of 354 patients with Alzheimer’s disease or mild cognitive impairment, only three patients, or 0.8 percent, showed comparable behaviors. The difference between the groups was highly significant, and the contrast with estimated general-population prevalence figures of roughly six to seven percent for problematic internet use reinforces the notion that this is a specific behavioral feature of FTD rather than a general consequence of cognitive decline.

The clinical pictures described in the series are vivid and unsettling. Excessive social media use affected six patients, producing sleep deprivation, religious obsessions, extensive dating app use that led to extramarital affairs and divorce, and hours spent browsing job portals without any genuine intention of seeking employment. Four patients engaged in problematic gaming, with sessions exceeding twelve hours a day and accompanied by social withdrawal and severely disrupted daily routines. Two patients became trapped in binge-watching cycles of repetitive video streaming, and two displayed impulsive online buying that drew them into scams and unnecessary purchases. Half of the problematic-use group showed overlapping behaviors, and social isolation emerged as a pervasive consequence across nearly every case.

Perhaps most telling were the relational dynamics that surrounded these behaviors. Nine of the fourteen patients reported preferring online interactions to face-to-face communication, deepening their withdrawal from family and social life. Six became frustrated and angry when caregivers attempted to limit smartphone access, echoing the agitation commonly seen when substance-dependent individuals are denied their drug of choice. Within the FTD group, patients exhibiting problematic internet use were significantly younger and had an earlier disease onset than those without such behaviors, though education, sex distribution, global cognition as measured by the Mini-Mental State Examination, and overall disease severity did not differ between the groups.

Subgroup analyses hinted at divergent mechanisms driving the behavior across the FTD spectrum. Among patients with behavioral variant FTD, problematic internet use was associated with a younger, early-onset phenotype, potentially reflecting a generational familiarity with digital technology or a specific behavioral trait independent of cognitive decline. Among patients with primary progressive aphasia, by contrast, problematic use correlated with greater disease severity, suggesting that in the language variants these behaviors may emerge as a nonverbal outlet, a way of compensating for the progressive loss of linguistic and social communication abilities as conversation becomes increasingly difficult.

The neurobiological plausibility of these findings is considerable. Previous research indicates that the neural circuits implicated in substance addictions, involving reward processing, impulse control, and decision-making, may also underlie problematic internet-related behaviors. These are precisely the circuits that deteriorate in FTD, particularly in the behavioral variant, where impulsivity, disinhibition, and compulsivity are hallmark features. The parallels between online addiction and more traditional forms of addiction support the hypothesis that both stem from shared neurobiological mechanisms, and the new series adds ecological validity to that framework by documenting real-world cases in a neurodegenerative population.

The authors acknowledge limitations, including the retrospective design, reliance on clinical records and caregiver report, inconsistent recording of specific applications and platforms, and the need for longitudinal research to track how these behaviors evolve as disease progresses. Even so, the societal implications are difficult to ignore. As internet use becomes ever more pervasive, patients with FTD may be especially vulnerable to maladaptive online behaviors that complicate clinical management, deepen social isolation, and increase caregiver burden. The researchers argue that as society adapts to new technologies, clinical frameworks for dementia must evolve in parallel, ensuring that emerging digital-age behavioral patterns are recognized in diagnosis and addressed in care.

Subject of Research: Problematic internet use as an emerging behavioral feature of frontotemporal dementia

Article Title: Problematic Internet Use in Frontotemporal Dementia: A Case Series

Article References: Urso, D., Volpe, G., Valguarnera, A., Vilella, D., Vitulli, A., Gnoni, V., Giugno, A., Rollo, E., Griffiths, M. D., & Logroscino, G. (2026). Problematic Internet Use in Frontotemporal Dementia: A Case Series. Annals of Clinical and Translational Neurology, 13(9), 1947-1950. https://doi.org/10.1002/acn3.70417

Image Credits: AI Generated

DOI: 10.1002/acn3.70417

Keywords: frontotemporal dementia, behavioral variant FTD, problematic internet use, behavioral addiction, primary progressive aphasia, social media, neurodegenerative disease, caregiver burden, compulsivity, digital behavior, Problematic, Internet

The biological definition of Alzheimer’s disease rests on two proteinopathies: extracellular amyloid-beta plaques and intracellular tau neurofibrillary tangles. Under modern biomarker-based criteria, a positive amyloid test combined with evidence of tau pathology is sufficient to establish the disease biologically, regardless of which symptoms a patient shows. Yet the two hallmark proteins do not strike the brain uniformly. In typical Alzheimer’s disease, tau accumulates early in the medial temporal lobe, and memory fails first. In atypical forms, the same molecular process unfolds with a strikingly different geographic signature, sparing the hippocampus and instead devastating posterior cortical regions, left-hemisphere language networks, frontal-executive circuits, or motor and praxis-related areas.

The review catalogs the principal atypical presentations. Posterior cortical atrophy begins with visual disturbances, including difficulty reading, judging spatial relationships, and recognizing objects, and is frequently misdiagnosed as ophthalmological disease for years. Logopenic variant primary progressive aphasia erodes word-finding and sentence repetition, often sending patients to speech-language pathologists before any dementia specialist is involved. Behavioral and dysexecutive Alzheimer’s disease mimics frontotemporal dementia, with disinhibition, apathy, impaired planning, or poor judgment dominating the early course. Corticobasal syndrome, classically associated with the tauopathy corticobasal degeneration, can in a subset of cases prove at autopsy to be driven by Alzheimer’s pathology. Each variant tends to strike at a younger age than typical disease, often in the fifties and sixties, when patients are still working and raising families.

A central technical insight of the review is the dissociation between amyloid and tau as explanatory variables. Amyloid biomarkers, whether cerebrospinal fluid assays or amyloid PET, usually confirm that Alzheimer’s biology is present, but amyloid burden correlates poorly with symptom type and severity. Regional tau burden, measured by tau PET or quantified at autopsy, tracks the affected brain network far more closely. In posterior cortical atrophy, tau concentrates in occipital and parietal cortex; in logopenic aphasia, in left temporoparietal language areas; in behavioral variants, in frontal and medial prefrontal regions. Neurodegeneration and metabolic dysfunction, seen on MRI and FDG-PET, mirror this tau topography. In other words, amyloid may set the stage, but tau’s choreography determines which act the audience sees.

This network-based view draws on a foundational observation in neurodegeneration research: degenerative diseases appear to target large-scale brain networks rather than random collections of neurons. Tau pathology seems to propagate along connected circuits, and the selective vulnerability of particular networks, why posterior cortical networks fail while hippocampal ones hold out in a given patient, remains one of the field’s central unsolved questions. Atypical variants, the authors argue, are natural experiments in selective vulnerability. Because the same disease biology produces radically different regional outcomes, these patients offer a uniquely powerful window into which immune-glial, vascular, protein-handling, synaptic, or genetic factors tip particular circuits into early failure.

The evidence for such modifiers is accumulating. Neuropathological studies have shown that clinical variants of Alzheimer’s disease carry distinct regional patterns of neurofibrillary tangle accumulation and distinct neuroinflammatory profiles. Microglial activation, tracked by translocator protein PET, is elevated in posterior cortical atrophy in patterns that differ from amnestic disease, and inflammation appears to co-localize with tau in early-onset cases. Genetic findings add another layer: TREM2 risk variants, which alter microglial function, are associated with atypical presentations, while APOE epsilon4, the strongest common genetic risk factor for typical late-onset disease, shows a more complex relationship with phenotype, influencing tau and amyloid PET patterns and functional connectivity in posterior cortical atrophy and logopenic aphasia. Tau itself is molecularly diverse, with cryo-EM studies revealing distinct filament structures, and tau strain differences have been proposed to contribute to clinical heterogeneity.

Co-pathology further complicates the picture. Many older brains harbor more than one misfolded protein, and comorbid Lewy body pathology, vascular injury, or TDP-43 can reshape both the clinical presentation and the pace of decline. Studies of early-onset versus late-onset disease show differing burdens of comorbid neuropathology, and community-based autopsy studies reveal that many people with substantial Alzheimer’s pathology never developed dementia, highlighting the role of resilience and compensatory factors. Age itself matters: younger patients tend to have purer, more focal pathology, which may partly explain why atypical phenotypes cluster at younger ages of onset.

The review’s core proposal is a four-axis framework designed to capture this heterogeneity without abandoning the biological definition of the disease. The first axis is the clinical phenotype, the observable syndrome such as posterior cortical atrophy or logopenic aphasia. The second is the AD biological context, encompassing the presence of amyloid and tau, co-pathologies, and molecular modifiers such as genetic risk and inflammatory state. The third is network topography, the regional pattern of tau, atrophy, and dysfunction that defines which circuits are under attack. The fourth is tempo, the rate of clinical and biomarker progression, which ranges from indolent to rapidly progressive and is increasingly recognized as a distinct dimension of disease rather than a footnote. Recording all four axes, the authors contend, would allow two patients with identical amyloid status to be described in terms that actually predict their trajectories.

The practical stakes are considerable. Diagnostic delays in atypical Alzheimer’s are notorious, with posterior cortical atrophy patients often waiting years for a correct diagnosis while being treated for cataracts, anxiety, or stress. Biomarker frameworks built around the amyloid-tau-neurodegeneration scheme confirm biological Alzheimer’s disease but say little about phenotype, network, or pace, leaving clinicians and trialists with coarse categories. Clinical trials designed around memory outcomes may miss benefit in patients whose relevant endpoints are visual processing or language fluency, and cohorts mixing typical and atypical cases without stratification can dilute or obscure treatment effects. A recent call to action on improving the clinical trial landscape for atypical variants underscores the point: without network-tailored outcomes and phenotype-specific stratification, trials risk failing for reasons unrelated to the drug’s biology.

The framework also reframes a deeper conceptual question the authors have pressed before: whether Alzheimer’s disease, defined by a shared molecular pathology but expressed through such divergent clinical and anatomical routes, is best understood as one disease or a family of diseases. By separating what is common, the amyloid-tau biology, from what varies, the topography, tempo, and biological context, the multi-axis model offers a way to keep a unified biological diagnosis while acknowledging genuine subtypes within it. For the growing population of patients diagnosed with Alzheimer’s disease in their fifties and sixties with symptoms that bear no resemblance to the textbook memory disorder, that shift in descriptive precision is not academic. It determines whether their disease is recognized early, whether they are enrolled in the right trials, and whether the outcomes measured in those trials reflect the brain networks actually failing beneath their symptoms.

Subject of Research: A multi-axis framework for defining clinical, pathological, network, and progression heterogeneity in atypical Alzheimer disease

Article Title: Atypical Alzheimer disease: a multi-axis framework toward defining heterogeneity

Article References: Grinberg, L. T., & Murray, M. E. (2026). Atypical Alzheimer disease: a multi-axis framework toward defining heterogeneity. Nature Reviews Neurology. https://doi.org/10.1038/s41582-026-01267-y

Image Credits: AI Generated

DOI: 10.1038/s41582-026-01267-y

Keywords: Alzheimer's disease, atypical Alzheimer disease, posterior cortical atrophy, primary progressive aphasia, tau pathology, amyloid-beta, biomarkers, selective vulnerability, brain networks, neuroinflammation, APOE, clinical trials

Primary progressive aphasia, or PPA, is not a single disease but a clinical syndrome caused by progressive damage to language networks in the brain. Its principal variants are distinguished by the nature of the language impairment. People with nonfluent PPA often produce slow, effortful, grammatically simplified speech. Those with logopenic PPA commonly struggle to retrieve words and repeat longer phrases, while semantic PPA is characterized by a progressive loss of word meaning and knowledge about objects or concepts. In practice, however, these boundaries can be blurred, especially early in the illness.

The new study tested whether computational analysis could transform short samples of connected speech into interpretable profiles that distinguish these variants. Rather than relying only on a single measure, such as speaking rate or the number of pauses, the researchers evaluated multiple features of speech and language. These may include lexical choices, sentence structure, grammatical complexity, fluency, pauses, repetitions and other characteristics that change as specific language networks deteriorate. The goal was not simply to classify participants with a black-box algorithm, but to create variant-specific scores that could be examined and related to brain biology.

To generate the profiles, the investigators used a statistical technique called Lasso multinomial modeling. This method is designed to select the most informative variables from a large set of potentially correlated features while limiting overfitting. In a multinomial model, the algorithm can distinguish among more than two outcome categories—in this case, nonfluent, logopenic and semantic PPA. The Lasso penalty shrinks weaker or redundant predictors, producing a more compact model that may be easier to interpret and apply to new speech samples.

The resulting speech profiles showed high overall performance in differentiating the PPA variants. Importantly, the patterns were not merely statistical labels detached from neuroscience. The investigators found expected associations between the speech scores and regional brain atrophy, suggesting that specific features of a person’s language may reflect damage in the neural systems that support speech production, word retrieval, sentence construction and semantic knowledge. These links strengthen the possibility that automated speech analysis could serve as a noninvasive window into disease-related changes in the brain.

The study also examined an autopsy-confirmed subset of participants, providing an unusually direct test of whether speech profiles correspond to underlying neuropathology. PPA can arise from different protein-accumulation diseases, including forms associated with frontotemporal lobar degeneration and Alzheimer disease pathology. Although clinical symptoms and pathology do not map perfectly onto one another, the researchers found that the speech profiles discriminated among common neuropathologic classes in the autopsy-confirmed group. This result suggests that speech may contain clues not only about the clinical presentation but also about the biological process driving degeneration.

The approach could be particularly valuable because connected speech is relatively easy to collect. A participant might describe a picture, recount a recent event or discuss a familiar topic while being recorded on a computer or smartphone. Automated systems can then quantify language features consistently across visits, potentially reducing the time required for specialized testing. Repeated recordings could also help investigators track whether a patient’s speech profile is changing, offering a practical measure for clinical monitoring and future therapeutic trials.

The findings do not mean that an algorithm can replace a neurologist, speech-language pathologist, brain imaging or biomarker testing. The study was cross-sectional, meaning that participants were assessed at a particular point rather than followed over time to determine how their profiles evolved. Speech can also be influenced by education, multilingualism, hearing ability, mood, fatigue, technology and differences in conversational style. Before clinical deployment, automated tools will need validation in larger and more diverse populations, as well as testing across microphones, languages, settings and stages of disease.

Even with those limitations, the work points toward a new model of neurological assessment in which everyday language becomes a measurable clinical signal. A short recording could complement imaging and laboratory biomarkers, help flag patients for specialist evaluation and provide an interpretable summary of how language networks are functioning. Because the study connected computational speech measures with both brain atrophy and autopsy-confirmed pathology, it offers evidence that the technology may be biologically grounded rather than merely predictive. Published in JAMA Neurology, the research highlights how artificial intelligence and careful statistical modeling could make the subtle signatures of neurodegeneration easier to detect, explain and follow.

Subject of Research: Automated connected-speech analysis for distinguishing primary progressive aphasia variants and identifying associations with brain atrophy and neuropathology.

Web References: https://doi.org/10.1001/jamaneurol.2026.2520

References: JAMA Neurology study identified by DOI 10.1001/jamaneurol.2026.2520.

Keywords: Primary progressive aphasia; aphasia; speech analysis; language disorders; computational neurology; machine learning; Lasso multinomial modeling; brain atrophy; neuropathology; Alzheimer disease; frontotemporal lobar degeneration; neurological diagnosis; patient monitoring.