Over 52 million people in the US are age 65 or over and at risk for Mild Cognitive Impairment, a condition affecting an estimated 12 million of them. With disease-modifying therapies coming of age to slow the progression from MCI to dementia, identifying those with MCI is particularly critical as currently it is estimated that only 10% of those affected are diagnosed. An ultrabrief screening tool that could be performed in just 2-3 minutes would empower primary care clinicians to improve routine screening for MCI. We used a data-driven approach to create a novel ultra-brief tool, combining the most information-efficient test-items identified by analysis of NIH Uniform Data Set (UDS) version 3 study items in > 10,000 MCI and CN individuals from the National Alzheimer's Coordinating Center (NACC) data repository. In a manner similar to the development of the brief optimized cognitive composite (BOCC), here we created an ultra-brief BOCC (uBOCC). This test does not require informant information and consists of just two optimally combined items (a 5-word memory test and a category fluency test). We then measured the area under the receiver operating characteristics curve (AUC-ROC) value for the uBOCC in detecting MCI. The uBOCC showed an AUC-ROC of 0.83 in predicting a diagnosis of MCI in the NACC. This accuracy is at least equal to the MoCA (0.82), while requiring only 2-3 minutes. The test also performs exceptionally well at detecting future progression to dementia (a CDR-Global score of 1 or greater) in 5 years with an AUC-ROC of 0.91. An ultra-brief screening tool, consisting of just two items optimally combined, can perform comparably to the MoCA and far outperform other brief screens such as the Mini-Cog. Such a tool could be incorporated into routine screening in those over 65 to dramatically enhance MCI detection.

Alzheimer's disease (AD) is linked to abnormal cortical excitability. Recent studies have suggested that amyloid-related cortical hyperexcitability may drive faster clinical decline and be related to spreading of tau. However, we lack non-invasive methods to quickly and directly assay cortical excitability in AD patients. We generate an Input-Output response curve (I/O Curve) using transcranial magnetic stimulation (TMS) with electromyography (EMG) to investigate mechanisms of cortical excitability in AD. Participants included 52 biomarker-positive early AD (CDR=0.5-1, age 70±8, 37% female) and 51 cognitively normal older adults (CN; age 70±6.5, 53% female). Single-pulse TMS was applied to left motor cortex to measure resting motor threshold (RMT). An I/O Curve was generated by delivering 10 pulses each at eight stimulation intensities (30-100% maximum stimulator output (%MSO)). We recorded motor-evoked potentials (MEP) from the right first dorsal interosseous muscle. RMTs were compared between AD and CN using a linear model controlling for scalp-to-cortex distance (SCD) and protocol (i.e., EEG cap). For the I/O curve, stimulation intensity was normalized to RMT (%MSO/RMT) and binned in 15% increments. MEP amplitudes along the I/O Curve were analyzed using a linear mixed effects model with effects of Group, %MSO/RMT, and Group*%MSO/RMT; covariates of age, sex, education, and APOE4 alleles; and participant-level random effects. Contrasts were conducted comparing the groups at each point along the I/O curve. RMT was lower in AD than in CN (η 2 p=0.12, medium effect size, p  = 0.004, Figure 1); SCD was a significant covariate ( p  = 0.006). The I/O Curve revealed an increased response in the AD group, with significant effects of Group (η 2 p=0.10, medium effect size; p  = 0.007), %MSO/RMT (η 2 p=0.71, p  <0.001), and Group*%MSO/RMT (η 2 p=0.05, small effect size, p  = 0.013). Covariates were not significant. Increased excitability in AD was greatest at higher stimulation intensities (120-135%MSO/RMT: Cohen's d=0.286, small effect size, p  = 0.015; 135-150%MSO/RMT: Cohen's d=0.495, medium effect size, p  <0.001, Figure 2). TMS-EMG confirms evidence of increased cortical excitability in AD. This was evident at lower intensities with the RMT model (implicating voltage-gated Na+ channels) and higher intensities with the I/O Curve (implicating AMPA receptors). TMS may be useful to measure target engagement of novel therapies targeting cortical excitability in AD.

BackgroundCognitive testing is critical for screening, diagnosing, and monitoring neurocognitive disorders, but current tests are limited by long administration times and the need for trained personnel. Digital cognitive testing offers a convenient and highly repeatable alternative to track cognitive decline, which is essential for evaluating patient care and in clinical trials. To address these limitations, we developed a novel digital spatial working memory test called the D-Cog.ObjectiveThis study aimed to assess the feasibility, validity, and diagnostic utility of the D-Cog in a clinical population.MethodsPatients with Alzheimer's disease (AD), Lewy body dementia (DLB), Parkinson's disease (PD), and cognitively normal controls (CN) were included. During clinical visits, a medical assistant administered the D-Cog test, presented as a "serious game" on an iPad.ResultsThe D-Cog showed high participant engagement, although engagement decreased with advancing cognitive impairment. A significant difference in the mean D-Cog score was observed between CN and both AD and DLB/PD groups (p < 0.003). Additionally, a significant correlation between the D-Cog score and the Mini-Mental State Examination was identified (r = 0.65, p < 0.001, 95% CI: 0.52-0.75). The D-Cog demonstrated good discrimination between groups, with AUCs of 0.96 (95% CI: 0.91-1.00) for AD versus healthy controls and 0.88 (95% CI: 0.75-0.97) for MCI versus healthy controls.ConclusionsThe D-Cog is a feasible and valid digital cognitive test with potential clinical utility across neurocognitive disorders. Although engagement was lower in advanced dementia, these findings support further development and exploration of the D-Cog, particularly for potential in-home assessments.

Recent studies suggest that cortical hyperexcitability in Alzheimer's disease (AD) may accelerate disease progression. However, the field lacks validated non-invasive methods to assay excitability in humans. In this study we used transcranial magnetic stimulation with electromyography to investigate mechanisms of motor cortical excitability in 63 biomarker-positive early-AD (ranging from mild cognitive impairment to mild dementia) participants and 72 cognitively unimpaired age-matched adults (CU). Single-pulse stimulation was applied to motor cortex to record resting motor thresholds (rMT) and motor-evoked potentials. An Input-Output curve was generated by delivering 10 pulses each at eight different intensities of maximum stimulator output (%MSO). Linear models or equivalent non-parametric tests 1) compared excitability metrics between groups, 2) tested correlations with cognition (mini-mental state exam, MMSE; Alzheimer's disease assessment scale-cognitive, ADAS-Cog), and 3) tested the impact of APOE4. Results show that early-AD participants have increased motor cortical excitability than CU, with lower rMT (p < 0.001), lower Input-Output curve Inflection Point (p = 0.007), and higher Dynamic Range (p = 0.035). An analysis of the Input-Output curve adjusting for rMT showed larger responses in AD specifically in the 135-150% rMT range (p = 0.023). In AD, higher excitability was related to worse cognition (rMT: MMSE p = 0.008, Inflection Point: MMSE p = 0.030 and ADAS-Cog p = 0.041). There was no relationship between APOE4 and excitability. In conclusion, AD participants have increased motor cortical excitability, related to cognition. This is evident both at lower and higher stimulation intensities. TMS may provide a useful measure of target engagement for therapies aimed at preserving cognition or slowing decline in early-AD.

Patients with Early Alzheimer's Disease (AD) can have fluctuations in their cognitive abilities. A rapid, significant change could indicate a serious health condition ranging from a urinary tract infection to delirium from brain edema due to an amyloid-reducing therapy (ARIA). Unfortunately, there are no clinical tools currently in use to frequently assess cognition at home. Such a tool could not only detect acute changes from delirium, but also gradual changes related to disease progression. This study aims to investigate the utility, convenience, and feasibility of long-term use of a novel online cognitive assessment (D-Cog) in detecting cognitive changes at home. We have designed a simple cognitive test, the D-Cog, in the form of a “cognitive vital sign” that can be performed regularly at home in under 2 minutes. Pilot data on the utility of the D-Cog was collected from 82 patients (mean age 74, 54 female), clinically diagnosed with MCI or AD by clinicians in the Beth Israel Deaconess Medical Center (BIDMC) Cognitive Neurology Unit (CNU). In addition, 15 participants with MCI or AD will be recruited to complete the D-Cog daily for 6 months at home with study staff monitoring the fluctuations in data remotely. 28 participants were unable to complete the task following a demonstration and/or prompting from study staff and were subsequently excluded. The 54 included participants (mean age 76, 31 female) averaged a D-Cog score of 3.9 and a MMSE score of 22. D-Cog and MMSE scores were found to be highly correlated ( r  = 0.56, p  =  1.3e-5). In anecdotal reports following D-Cog administration, patients indicate the task would be easy for them to complete at home. The D-Cog assessment is feasible in clinical practice for AD patients, and patients would be willing to complete the D-Cog remotely while at home. D-Cog scores compare well against standardized pen-and-paper neuropsychological scores but offer the convenience of at-home use and more frequent self-administration. Future work investigating the utility and convenience of the D-Cog as a daily at-home assessment is promising as an accurate measure of cognitive ability, potentially offering early detection of delirium or ARIA.

Mild cognitive impairment (MCI), an early sign of Alzheimer's Disease (AD), involves disruptions in both memory and cognition. Early and accurate detection of MCI is critical, as recently FDA approved disease modifying treatments are most effective at this stage of AD. The most commonly used screening tests for MCI are the Montreal Cognitive Assessment (MoCA) and the Mini-Mental Status Examination (MMSE). However, these tests are time consuming, and item level analysis has not been conducted to see whether the components of these tests are sufficiently accurate in detecting MCI. This study aims to analyze the accuracy and actual contribution of individual items on the MoCA and the MMSE in MCI detection. Using the National Alzheimer's Coordinating Center's (NACC) data repository, our team conducted item level analysis of the MoCA and MMSE given to MCI as well as cognitively normal (CN) participants. We used area under the receiver operating characteristic curves (AUC-ROC) to analyze the accuracy of these tests. This method enabled us to determine the overall accuracy in terms of MCI detection of the tests as well as the individual contribution of each item on the test. The overall MoCA AUC-ROC was 0.81 while the MMSE AUC-ROC was 0.76, which shows that both tests are moderately accurate. Item level analysis of the tests revealed that only a small subset of components effectively contributes to MCI detection. We found that a six-item combination of memory recall and orientation testing components contributes to over 98% of full test accuracy. In this study, we found that the MoCA and the MMSE have moderate efficacy in detecting MCI, and that only a subset of items from each test are the large contributors of MCI detection. Our analysis shows that the number of items on these tests could be substantially reduced which would lower time of test administration without loss of MCI detection effectiveness. There are opportunities for MCI screening test optimization, and further investigation is needed to determine the best possible combination of test items that would yield the highest detection accuracy in the lowest amount of time.

Cognitive tests of naming ability have been shown to have diagnostic and prognostic utility in both mild cognitive impairment (MCI) and Alzheimer’s disease (AD; Taler & Phillips, 2008). The Boston Naming Test (BNT) is the most common naming test, which consists of 60 black-and-white drawings and takes 20-30 minutes to administer. Retrospective analysis has shown that administering the BNT in an adaptive fashion could result in a comparable measure of the patient’s naming ability in only 8 items instead of 60. A prospective administration of this adaptive naming test (ANT) was necessary to assess its utility in clinical practice. Using item response theory (IRT) and published information about each BNT item’s difficulty and discriminability (Pedraza et al., 2011), we created an algorithm to administer the traditional BNT in an adaptive format. Patients were recruited from the Cognitive Neurology clinic at Beth Israel Deaconess Medical Center. Participants completed both a 30-item traditional BNT (either odd or even-numbered items) and a 10-item ANT (selected from the remaining set of 30 items). Randomization was used to decide which test would be first, and which would use the odd or even-numbered items. Z-scores were calculated for BNT scores using normative data from Katsumata et al. (2015). ANT scores were calculated according to IRT principles (Pedraza et al., 2011). Total administration time from the two tests were compared using paired t-tests, and descriptive statistics results are presented as mean±SD. We have begun testing patients with the traditional and adaptive forms of the BNT (n = 7). 28% of patients have MCI and 57% have mild-to-moderate AD. Average administration times were 611±222 seconds for the BNT and 59±13 seconds for the ANT (difference = 552 seconds, p = 0.0013). After removing one outlier, the standardized BNT scores and ANT scores exhibit a linear relationship with an r-squared value of 0.977. This prospective administration of the ANT confirms prior research that an adaptive BNT performs significantly faster while still giving a reliable measure of one’s naming ability. We expect ongoing data collection to further strengthen the robustness of our findings.

Mild Cognitive Impairment (MCI) affects over 12 million individuals in the US, 50% of whom will progress to Alzheimer's disease or another form of dementia in 3-5 years. But 90% of individuals with MCI remain undiagnosed due to challenges in screening. With the advent of disease-modifying therapies (DMTs) that can slow progression, it is now critical that new and improved tools become available to quickly and accurately screen for MCI, and especially for the subpopulation of individuals at highest risk for developing future dementia. Here we use data-driven design to create a novel tool for cognitive assessment that combines information-efficient test-items identified by analysis of the National Alzheimer's Coordinating Center (NACC) NIH Uniform Data Set version 3 (UDSv3) study items in > 10,000 CN, MCI, and early dementia individuals. We assessed the ability to detect MCI measured by the receiver operating characteristic (ROC) area under the curve (AUC) values, selected the best-performing items, and determined the optimal weighting between them. Using both data-driven item selection and optimal item weighting allowed for the creation of a 4-item brief optimized cognitive composite (BOCC) test with a 4-5 minute administration time that can dramatically outperform the 10-14 minute MoCA at detecting both MCI and mild dementia. We find AUC values for the BOCC that are 35% closer to the ideal 1.00 value for BOCC compared to MoCA scores for detecting MCI (0.87 vs 0.80, p  <0.0001) and 80% closer to ideal for BOCC vs MoCA scores for detecting mild dementia (0.99 vs 0.95, p  <0.0001). Remarkably, the BOCC score also provides more information for predicting 6-year future conversion to dementia for MCI and CN individuals than the dementia specialists’ clinical diagnoses (AUCs: 0.95 vs 0.88, p  <0.0001). A 4-5 minute brief optimized cognitive test can dramatically outperform the 10-14 minute MoCA in detecting MCI, and it can predict future decline to dementia comparably to, or better than, a clinical diagnosis made by clinicians with access to extensive cognitive and clinical information. This test could improve MCI detection in the more than 52 million people in the US age 65 or over.

BACKGROUND:Mild Cognitive Impairment (MCI) affects over 12 million individuals in the US, 50% of whom will progress to Alzheimer's disease or another form of dementia in 3-5 years. But 90% of individuals with MCI remain undiagnosed due to challenges in screening. With the advent of disease-modifying therapies (DMTs) that can slow progression, it is now critical that new and improved tools become available to quickly and accurately screen for MCI, and especially for the subpopulation of individuals at highest risk for developing future dementia. METHODS:Here we use data-driven design to create a novel tool for cognitive assessment that combines information-efficient test-items identified by analysis of the National Alzheimer's Coordinating Center (NACC) NIH Uniform Data Set version 3 (UDSv3) study items in > 10,000 CN, MCI, and early dementia individuals. We assessed the ability to detect MCI measured by the receiver operating characteristic (ROC) area under the curve (AUC) values, selected the best-performing items, and determined the optimal weighting between them. RESULTS:Using both data-driven item selection and optimal item weighting allowed for the creation of a 4-item brief optimized cognitive composite (BOCC) test with a 4-5 minute administration time that can dramatically outperform the 10-14 minute MoCA at detecting both MCI and mild dementia. We find AUC values for the BOCC that are 35% closer to the ideal 1.00 value for BOCC compared to MoCA scores for detecting MCI (0.87 vs 0.80, p <0.0001) and 80% closer to ideal for BOCC vs MoCA scores for detecting mild dementia (0.99 vs 0.95, p <0.0001). Remarkably, the BOCC score also provides more information for predicting 6-year future conversion to dementia for MCI and CN individuals than the dementia specialists' clinical diagnoses (AUCs: 0.95 vs 0.88, p <0.0001). CONCLUSION:A 4-5 minute brief optimized cognitive test can dramatically outperform the 10-14 minute MoCA in detecting MCI, and it can predict future decline to dementia comparably to, or better than, a clinical diagnosis made by clinicians with access to extensive cognitive and clinical information. This test could improve MCI detection in the more than 52 million people in the US age 65 or over.

Small single-site studies found that transcranial magnetic stimulation (TMS) targets with better antidepressant response were more negatively functionally connected to the subgenual cingulate cortex (SGC). These led to "anti-subgenual" TMS targeting in recent clinical trials. We conducted a larger prospective multi-site observational study to test the robustness of this observation in more diverse clinical populations. Sixty-six treatment-seeking individuals with major depressive disorder (MDD) received 3-8 weeks of daily rTMS to the left dorsolateral prefrontal cortex using scalp-based targeting as part of standard clinical care. Stimulation sites were recorded with MRI neuronavigation on multiple days. Our primary outcome was the correlation between change in Beck Depression Inventory (BDI-II) score and connectivity of each individual's TMS site to the SGC, computed using resting-state functional connectivity data from 1000 healthy individuals. Secondary (post hoc) analyses incorporated additional clinical covariates. No relationship was found between antidepressant response and normative connectivity of TMS site to SGC (r = 0.1, p = 0.39). This was not due to inconsistency in the location of the TMS sites, which showed smaller within- than between-individual variance (p < 0.0001). Post hoc analyses showed significant associations when adding clinical covariates (r = -0.27, p = 0.014). Baseline anxiety (p < 0.0001) and comorbid psychiatric conditions (p < 0.001) accounted for the most variance in response. Atlas-based connectivity of TMS site to the SGC accounted for minimal variance in antidepressant response in this diverse sample. The "anti-subgenual" target derived based on normative connectome may be suboptimal for MDD patients with high baseline anxiety or psychiatric comorbidities. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT03276793.

Cortical excitability is elevated in Alzheimer’s disease (AD). Transcranial magnetic stimulation-evoked responses on electromyography (EMG) and electroencephalography (EEG) have captured this increased excitability in motor brain regions. However, it is not yet known if increased excitability is also present in the parietal lobe or the extent to which excitability is related to cognition. TMS-EEG data from 29 participants with biomarker-confirmed AD (CDR 0.5-1, age 57-81, 45% female) and 38 cognitively unimpaired older controls (CDR 0, age 57-89, 59% female) were analyzed. Single-pulse TMS was applied to left motor cortex (M1) and inferior parietal lobule (IPL) at 120% of resting motor threshold (RMT) and 135% of RMT (in 12 AD and 22 controls). The early (15-40 msec) TMS-evoked local mean field amplitude (LMFA) was assessed from electrodes near each TMS target. Group differences in LMFA were assessed separately for each site and intensity, controlling for age, sex, and scalp-to-cortex distance. A subset of AD participants had cognitive testing scores available for the Assessment Scale-Cognitive subscale (ADAS-Cog, n=10) and a Parietal Composite Score (averaged z-scores of Benton Judgement of Line Orientation, ADAS-Cog Maze 3 time, and WRAT 4 Math, n=13). Cognitive scores were related to LMFA at each site and intensity using separate linear models. In M1, 135% RMT evoked a larger LMFA in AD than in older controls (R 2 adj=0.17, p=0.036). Visual inspection of the M1 135% RMT evoked responses also revealed higher local responses in AD (Figure 1). There were no between-group differences for other conditions (p-values > 0.100). In AD, higher M1 LMFA at 135% RMT was related to worse global cognition on the ADAS-Cog (R 2 adj=0.68, p=0.002, Figure 2a), but not the Parietal Composite Score. Higher IPL LMFA at 120% RMT was related to worse performance on the Parietal Composite Score (R 2 adj =0.38, p=0.015, Figure 2b), but not the ADAS-Cog. TMS-EEG reveals elevated motor-area excitability in AD. Excitability measures showed a double-dissociation with cognition: motor excitability is related to global cognition and parietal excitability is related to parietal function. TMS-EEG may be useful to measure target engagement for future therapies seeking to restore normal neuronal excitability in AD.

ABSTRACTBackgroundAlzheimer's disease (AD) is characterized by impaired inhibitory circuitry and GABAergic dysfunction, which is associated with reduced fast brain oscillations in the gamma band (γ, 30–90 Hz) in several animal models. Investigating such activity in human patients could lead to the identification of novel biomarkers of diagnostic and prognostic value. The current study aimed to test a multimodal “Perturbation‐based” transcranial Alternating Current Stimulation‐Electroencephalography (tACS)‐EEG protocol to detect how responses to tACS in AD patients correlate with patients' clinical phenotype.MethodsFourteen participants with mild to moderate dementia due to AD underwent a baseline assessment including cognitive status, peripheral neuroinflammation, and resting‐state (rs)EEG. The tACS‐EEG recordings included brief (6′) tACS blocks of gamma (i.e., 40 Hz) stimulation administered through 4 different montages, with Pre/Post 32‐Channels EEG for each block. Changes in tACS‐EEG and rsEEG γ band power with respect to baseline were adopted as a metric of induction and compared with cognitive scores and neuroinflammatory biomarkers.ResultsWe found positive correlations between 40 Hz‐induced γ activity in fronto‐central‐parietal areas and patient cognitive status and negative ones with neuroinflammatory markers. Participants with greater cognitive impairment exhibited less γ induction and higher peripheral neuroinflammation. The same analysis performed with spectral power from baseline rsEEG resulted in no significant correlations, promoting the value of tACS‐based perturbation for capturing individual differences in pathology‐related brain features.ConclusionsOur work suggests a link between tACS‐induced γ band spectral power and clinical severity, with weaker γ induction corresponding to more severe clinical/cognitive impairment. This study provides preliminary support for the development of novel physiological biomarkers and therapeutic targets based on disease severity.

Neural hyperexcitability and network dysfunction are neurophysiological hallmarks of Alzheimer's disease (AD) in animal studies, but their presence and clinical relevance in humans remain poorly understood. We introduce a perturbation-based approach combining transcranial magnetic stimulation and electroencephalography (TMS-EEG), alongside resting-state EEG (rsEEG), to investigate neurophysiological basis of default mode network (DMN) dysfunction in early AD. While rsEEG revealed global neural slowing and disrupted synchrony, these measures reflected widespread changes in brain neurophysiology without network-specific insights. In contrast, TMS-EEG identified network-specific local hyperexcitability in the parietal DMN and disrupted connectivity with frontal DMN regions, which uniquely predicted distinct cognitive impairments and mediated the link between structural brain integrity and cognition. Our findings provide critical insights into how network-specific neurophysiological disruptions contribute to AD-related cognitive dysfunction. Perturbation-based assessments hold promise as potential markers of early detection, disease progression, and target engagement for disease-modifying therapies aiming to restore abnormal neurophysiology in AD.

BACKGROUND & OBJECTIVE:Alzheimer's Disease (AD) patients at multiple stages of disease progression have a high prevalence of seizures. However, whether AD and epilepsy share pathophysiological changes remains poorly defined. In this study, we leveraged high-throughput transcriptomic data from sporadic AD cases at different stages of cognitive impairment across multiple independent cohorts and brain regions to examine the role of epilepsy-causing genes. METHODS:Epilepsy-causing genes were manually curated, and their expression levels were analyzed across bulk transcriptomic data from three AD cohorts and three brain regions. RNA-seq data from sporadic AD and control cases from the Knight ADRC, MSBB, and ROSMAP cohorts were processed and analyzed under the same analytical pipeline. An integrative clustering approach employing machine learning and multi-omics data was employed to identify molecularly defined profiles with different cognitive scores. RESULTS:We found several epilepsy-associated genes/pathways significantly dysregulated in a group of AD patients with more severe cognitive impairment. We observed 15 genes consistently downregulated across the three cohorts, including sodium and potassium channels genes, suggesting that these genes play fundamental roles in cognitive function or AD progression. Notably, we found 25 of these genes dysregulated in earlier stages of AD and become worse with AD progression. CONCLUSION:Our findings revealed that epilepsy-causing genes showed changes in the early and late stages of AD progression, suggesting that they might be playing a role in AD progression. We can not establish directionality or cause-effect with our findings. However, changes in the epilepsy-causing genes might underlie the presence of seizures in AD patients, which might be present before or concurrently with the initial stages of AD.

Behavioral neurology & neuropsychiatry (BNNP) is a field that seeks to understand brain-behavior relationships, including fundamental brain organization principles and the many ways that brain structures and connectivity can be disrupted, leading to abnormalities of behavior, cognition, emotion, perception, and social cognition. In North America, BNNP has existed as an integrated subspecialty through the United Council for Neurologic Subspecialties since 2006. Nonetheless, the number of behavioral neurologists across academic medical centers and community settings is not keeping pace with increasing clinical and research demand. In this commentary, we provide a brief history of BNNP followed by an outline of the current challenges and opportunities for BNNP from the behavioral neurologist's perspective across clinical, research, and educational spheres. We provide a practical guide for promoting BNNP and addressing the shortage of behavioral neurologists to facilitate the continued growth and development of the subspecialty. We also urge a greater commitment to recruit trainees from diverse backgrounds so as to dismantle persistent obstacles that hinder inclusivity in BNNP-efforts that will further enhance the growth and impact of the subspecialty. With rapidly expanding diagnostic and therapeutic approaches across a range of conditions at the intersection of neurology and psychiatry, BNNP is well positioned to attract new trainees and expand its reach across clinical, research, and educational activities.

Integrating artificial intelligence (AI) technologies into neurology promises increased patient access, engagement, and quality of care, as well as improved quality of work life for clinicians. While most studies have focused on comparing AI models to expert performance, we argue for a more practical approach: demonstrating how AI can augment clinical practice. This article presents a framework for pragmatic AI augmentation, addressing the shortage in neurology practices, exploring the potential of AI in opportunistic screening, and encouraging the concept of AI serving as a "co-pilot" in neurology. We discuss recommendations for future studies designed to emphasize human-computer collaboration, ensuring AI enhances rather than replaces clinical expertise.

Background In epilepsy, the ictal phase leads to cerebral hyperperfusion while hypoperfusion is present in the interictal phases. Patients with Alzheimer's disease (AD) have an increased prevalence of epileptiform discharges and a study using intracranial electrodes have shown that these are very frequent in the hippocampus. However, it is not known whether there is an association between hippocampal hyperexcitability and regional cerebral blood flow (rCBF). The objective of the study was to investigate the association between rCBF in hippocampus and epileptiform discharges as measured with ear-EEG in patients with Alzheimer's disease. Our hypothesis was that increased spike frequency may be associated with increased rCBF in hippocampus. Methods A total of 24 patients with AD, and 15 HC were included in the analysis. Using linear regression, we investigated the association between rCBF as measured with arterial spin-labelling MRI (ASL-MRI) in the hippocampus and the number of spikes/sharp waves per 24 h as assessed by ear-EEG. Results No significant difference in hippocampal rCBF was found between AD and HC (p-value = 0.367). A significant linear association between spike frequency and normalized rCBF in the hippocampus was found for patients with AD (estimate: 0.109, t-value = 4.03, p-value < 0.001). Changes in areas that typically show group differences (temporal-parietal cortex) were found in patients with AD, compared to HC. Conclusions Increased spike frequency was accompanied by a hemodynamic response of increased blood flow in the hippocampus in patients with AD. This phenomenon has also been shown in patients with epilepsy and supports the hypothesis of hyperexcitability in patients with AD. The lack of a significant difference in hippocampal rCBF may be due to an increased frequency of epileptiform discharges in patients with AD. Trial registration The study is registered at clinicaltrials.gov (NCT04436341).

Parvalbumin-positive (PV+) basket neurons are fast-spiking, non-adapting inhibitory interneurons whose oscillatory activity is essential for regulating cortical excitation/inhibition balance. Their dysfunction results in cortical hyperexcitability and gamma rhythm disruption, which have recently gained substantial traction as contributing factors as well as potential therapeutic targets for the treatment of Alzheimer’s Disease (AD). Recent evidence indicates that PV+ cells are also impaired in Frontotemporal Dementia (FTD) and Dementia with Lewy bodies (DLB). However, no attempt has been made to integrate these findings into a coherent pathophysiological framework addressing the contribution of PV+ interneuron dysfunction to the generation of cortical hyperexcitability and gamma rhythm disruption in FTD and DLB. To fill this gap, we epitomized the most recent evidence on PV+ interneuron impairment in AD, FTD, and DLB, focusing on its contribution to the generation of cortical hyperexcitability and gamma oscillatory disruption and their interplay with misfolded protein accumulation, neuronal death, and clinical symptoms’ onset. Our work deepens the current understanding concerning the role of PV+ interneuron dysfunction across neurodegenerative dementias, highlighting commonalities and differences among AD, FTD, and DLB, thus paving the way for identifying novel biomarkers and potential therapeutic targets for the treatment of these diseases.

Functional MRI methods can assess aspects of drug-induced brain response. Resting blood oxygenation level dependent (BOLD) fMRI and arterial spin labeling (ASL) perfusion MRI indirectly measure brain function through the coupling of activity to cerebral blood flow (CBF) and oxygenation but their relative sensitivity has not been directly compared. We assessed changes in resting measures of BOLD and ASL MRI in response to two neurotransmitter modulators: citalopram, a selective serotonin reuptake inhibitor, and alprazolam, a positive allosteric modulator of GABA type A receptor. Thirty healthy subjects were imaged in a placebo-controlled study, with N = 20 subjects receiving each treatment as part of an incomplete block design. Time-averaged CBF images from ASL and measures of resting-state fluctuations of BOLD and ASL images were assessed for significant effects. Following acute citalopram administration, analysis of the ASL data showed a reduction in time-averaged regional CBF in regions associated with high levels of 5-HT1A receptor density. In contrast, following alprazolam administration, BOLD amplitude of low-frequency fluctuations showed a highly significant and cortically widespread increase, consistent with the distribution of GABA-A receptors. Only a marginal decrease in ASL CBF was detected after alprazolam intake. BOLD and ASL are each sensitive to drugs targeting neurotransmitter systems, but appear to reflect different aspects of neural metabolism and the balance between excitatory and inhibitory activity. Accordingly, their combination may best capture the effects of neurotransmitter modulations, and thus be advantageous for pharmacological MRI studies.