Adaptive cognitive control circuit changes associated with problem-solving ability and depression symptom outcomes over 24 months.

Mechanistically targeted behavioral interventions are a much-needed strategy for improving outcomes in depression, especially for vulnerable populations with comorbidities such as obesity. Such interventions may change behavior and outcome by changing underlying neural circuit function. However, it is unknown how these circuit-level modifications unfold over intervention and how individual differences in early circuit-level modifications may explain the heterogeneity of treatment effects. We addressed this need within a clinical trial of problem-solving therapy for participants with depression symptoms and comorbid obesity, focusing on the cognitive control circuit as a putative neural mechanism of action. Functional magnetic resonance imaging was applied to measure the cognitive control circuit activity at five time points over 24 months. Compared with participants who received usual care, those receiving problem-solving therapy showed that attenuations in cognitive control circuit activity were associated with enhanced problem-solving ability, which suggests that this circuit plays a key role in the mechanisms of problem-solving therapy. Attenuations in circuit activity were also associated with improved depression symptoms. Changes in cognitive control circuit activity at 2 months better predicted changes in problem-solving ability and depression symptoms at 6, 12, and 24 months, with predictive improvements ranging from 17.8 to 104.0%, exceeding baseline demographic and symptom characteristics. Our findings suggest that targeting the circuit mechanism of action could enhance the prediction of treatment outcomes, warranting future model refinement and improvement to pave the way for its clinical application.

Brain age is not a significant predictor of relapse risk in late-life depression.

INTRODUCTION: Late-life depression (LLD) has been associated cross-sectionally with lower brain structural volumes and accelerated brain aging compared to healthy controls (HC). There are few longitudinal studies on the neurobiological predictors of recurrence in LLD. We tested a machine learning (ML) brain age model and its prospective association with LLD recurrence risk. METHODS: We recruited individuals with LLD (n=102) and HC (n=43) into a multi-site 2-yr longitudinal study. Individuals with LLD were enrolled within 4 months of remission. Remitted LLD participants underwent baseline neuroimaging and longitudinal clinical follow-up. Over 2 years, 43 LLD participants relapsed (REL) and 59 stayed in remission (REM). We used a previously developed ML brain age algorithm to compute brain age at baseline and we evaluated brain age group differences (HC vs. LLD and HC vs. REM vs. REL). We conducted a Cox proportional hazards model to evaluate whether baseline brain age predicted time to relapse. RESULTS: We found that brain age did not significantly differ between HC and LLD as well as HC, REM, and REL groups. Brain age did not significantly predict time to relapse. DISCUSSION: In contrast to our hypothesis, we found that brain age did not differ between non-depressed controls and individuals with remitted LLD, and brain age was not associated with subsequent recurrence. This is in contrast to existing literature which has identified baseline brain age differences in late life but in line with work that shows no differences between those who do and do not relapse on gross structural measures.

Repetitive negative thinking and suicidal ideation in internalizing psychopathologies: A replication study.

Suicidal ideation (SI), a risk factor for suicide, is prevalent in internalizing psychopathologies, including depression and anxiety. Rumination and worry are well-studied repetitive negative thinking (RNT) constructs implicated in internalizing psychopathologies. These constructs have shared and distinct characteristics. However, the relationship between rumination and worry and their associations with SI are not fully understood in clinical samples. The present study used correlational and regression analysis to evaluate these relationships as a secondary data analysis in treatment-seeking participants with internalizing psychopathologies in two independent samples (Study 1:n = 143; Study 2:n = 133). Results showed about half of the participants endorsed SI (Study 1:n = 79; Study 2:n = 71). Correlations revealed a significant, positive relationship between rumination and worry. Regression results with SI as the dependent variable showed rumination significantly positively corresponded with SI in both studies. Post-hoc partial correlations controlling for symptom severity (depression, anxiety), worry, and age showed the rumination-SI relationship was maintained in both studies. Findings for worry and SI were inconsistent between studies. Findings indicate rumination, but not worry, could be a stable, unique contributor to SI in internalizing psychopathologies. It may be useful to incorporate RNT into suicide risk assessment for individuals with internalizing conditions.

Altered effective connectivity during threat anticipation in individuals with alcohol use disorder.

BACKGROUND: A developing theory and recent research suggest that heightened reactivity to uncertain stressors or threats may be an important individual difference factor that facilitates excessive drinking as a means of avoidance-based coping and characterizes individuals with current and past alcohol use disorder (AUD). Neuroimaging studies of unpredictable threat processing have repeatedly demonstrated activation of the anterior insula (AIC), anteromedial (AM) thalamus and dorsal anterior cingulate cortex (dACC). The present study aimed to understand how these three regions function as a network during anticipation of unpredictable threat (and predictable threat). METHODS: Participants were 43 young adults (aged 21-30) with AUD and 26 healthy controls. Functional magnetic resonance imaging and dynamic causal modeling were used to study inter-regional effective connectivities and predictable and unpredictable threat-related modulations thereof within this network. Parametric empirical Bayesian modeling was used to conduct between-group comparisons in effective connectivities. RESULTS: During unpredictable threat trials, the increased projection from the right AM thalamus to the right AIC was significantly present only in the AUD group. This directional influence was stronger among individuals who on average consumed more drinks per week. As expected, we found no group differences in modulatory changes to effective connectivities during predictable threat trials. CONCLUSIONS: To our knowledge, this is the first study to examine directional interactions between key frontolimbic regions during anticipation of unpredictable and predictable threat and demonstrate the importance of 'bottom-up' thalamic-insular projections during unpredictable threat processing in AUD. Prospective studies are warranted to determine whether this association may be causal.

Using a Novel Digital Go/No-Go to Dissociate Intra-subject Temporal Fluctuations in Reaction Time and Accuracy.

Impulsivity can be a risk factor for serious complications for those with mood disorders. To understand intra-individual impulsivity variability, we analyzed longitudinal data of a novel gamified digital Go/No-Go (GNG) task in a clinical sample (n=43 mood disorder participants, n=17 healthy controls) and an open-science sample (n=121, self-reported diagnoses). With repeated measurements within-subject, we disentangled two aspects of GNG: reaction time and accuracy in response inhibition (i.e., incorrect No-Go trials) with respect to diurnal and potential learning effects. Mixed-effects models showed diurnal effects in reaction time but not accuracy, with a significant effect of hour on reaction time in the clinical sample and the open-science sample. Moreover, subjects improved on their response inhibition but not reaction time. Additionally, significant interactions emerged between depression symptom severity and time-of-day in both samples, supporting that repeated administration of our GNG task can yield mood-dependent circadian rhythm-aware biomarkers of neurocognitive function.

Study of a PST-trained voice-enabled artificial intelligence counselor for adults with emotional distress (SPEAC-2): Design and methods.

BACKGROUND: Novel and scalable psychotherapies are urgently needed to address the depression and anxiety epidemic. Leveraging artificial intelligence (AI), a voice-based virtual coach named Lumen was developed to deliver problem solving treatment (PST). The first pilot trial showed promising changes in cognitive control measured by functional neuroimaging and improvements in depression and anxiety symptoms. METHODS: To further validate Lumen in a 3-arm randomized clinical trial, 200 participants with mild-to-moderate depression and/or anxiety will be randomly assigned in a 2:1:1 ratio to receive Lumen-coached PST, human-coached PST as active treatment comparison, or a waitlist control condition where participants can receive Lumen after the trial period. Participants will be assessed at baseline and 18 weeks. The primary aim is to confirm neural target engagement by testing whether compared with waitlist controls, Lumen participants will show significantly greater improvements from baseline to 18 weeks in the a priori neural target for cognitive control, right dorsal lateral prefrontal cortex engaged by the go/nogo task (primary superiority hypothesis). A secondary hypothesis will test whether compared with human-coached PST participants, Lumen participants will show equivalent improvements (i.e., noninferiority) in the same neural target from baseline to 18 weeks. The second aim is to examine (1) treatment effects on depression and anxiety symptoms, psychosocial functioning, and quality of life outcomes, and (2) relationships of neural target engagement to these patient-reported outcomes. CONCLUSIONS: This study offers potential to improve the reach and impact of psychotherapy, mitigating access, cost, and stigma barriers for people with depression and/or anxiety. CLINICALTRIALS: gov #: NCT05603923.

Brain activity during reappraisal and associations with psychotherapy response in social anxiety and major depression: a randomized trial.

BACKGROUND: Cognitive behavioral therapy (CBT) is an effective treatment for patients with social anxiety disorder (SAD) or major depressive disorder (MDD), yet there is variability in clinical improvement. Though prior research suggests pre-treatment engagement of brain regions supporting cognitive reappraisal (e.g. dorsolateral prefrontal cortex [dlPFC]) foretells CBT response in SAD, it remains unknown if this extends to MDD or is specific to CBT. The current study examined associations between pre-treatment neural activity during reappraisal and clinical improvement in patients with SAD or MDD following a trial of CBT or supportive therapy (ST), a common-factors comparator arm. METHODS: Participants were 75 treatment-seeking patients with SAD (n = 34) or MDD (n = 41) randomized to CBT (n = 40) or ST (n = 35). Before randomization, patients completed a cognitive reappraisal task during functional magnetic resonance imaging. Additionally, patients completed clinician-administered symptom measures and a self-report cognitive reappraisal measure before treatment and every 2 weeks throughout treatment. RESULTS: Results indicated that pre-treatment neural activity during reappraisal differentially predicted CBT and ST response. Specifically, greater trajectories of symptom improvement throughout treatment were associated with less ventrolateral prefrontal cortex (vlPFC) activity for CBT patients, but more vlPFC activity for ST patients. Also, less baseline dlPFC activity corresponded with greater trajectories of self-reported reappraisal improvement, regardless of treatment arm. CONCLUSIONS: If replicated, findings suggest individual differences in brain response during reappraisal may be transdiagnostically associated with treatment-dependent improvement in symptom severity, but improvement in subjective reappraisal following psychotherapy, more broadly.

A novel biomarker selection method using multimodal neuroimaging data.

Identifying biomarkers is essential to obtain the optimal therapeutic benefit while treating patients with late-life depression (LLD). We compare LLD patients with healthy controls (HC) using resting-state functional magnetic resonance and diffusion tensor imaging data to identify neuroimaging biomarkers that may be potentially associated with the underlying pathophysiology of LLD. We implement a Bayesian multimodal local false discovery rate approach for functional connectivity, borrowing strength from structural connectivity to identify disrupted functional connectivity of LLD compared to HC. In the Bayesian framework, we develop an algorithm to control the overall false discovery rate of our findings. We compare our findings with the literature and show that our approach can better detect some regions never discovered before for LLD patients. The Hub of our discovery related to various neurobehavioral disorders can be used to develop behavioral interventions to treat LLD patients who do not respond to antidepressants.

Assessing depression recurrence, cognitive burden, and neurobiological homeostasis in late life: Design and rationale of the REMBRANDT Study.

Background: Late-life depression is characterized by disability, cognitive impairment and decline, and a high risk of recurrence following remission. Aside from past psychiatric history, prognostic neurobiological and clinical factors influencing recurrence risk are unclear. Moreover, it is unclear if cognitive impairment predisposes to recurrence, or whether recurrent episodes may accelerate brain aging and cognitive decline. The purpose of the REMBRANDT study (Recurrence markers, cognitive burden, and neurobiological homeostasis in late-life depression) is to better elucidate these relationships and identify phenotypic, cognitive, environmental, and neurobiological factors contributing to and predictive of depression recurrence. Methods: Across three sites, REMBRANDT will enroll 300 depressed elders who will receive antidepressant treatment. The goal is to enroll 210 remitted depressed participants and 75 participants with no mental health history into a two-year longitudinal phase focusing on depression recurrence. Participants are evaluated every 2 months with deeper assessments occurring every 8 months, including structural and functional neuroimaging, environmental stress assessments, deep symptom phenotyping, and two weeks of 'burst' ecological momentary assessments to elucidate variability in symptoms and cognitive performance. A broad neuropsychological test battery is completed at the beginning and end of the longitudinal study. Significance: REMBRANDT will improve our understanding of how alterations in neural circuits and cognition that persist during remission contribute to depression recurrence vulnerability. It will also elucidate how these processes may contribute to cognitive impairment and decline. This project will obtain deep phenotypic data that will help identify vulnerability and resilience factors that can help stratify individual clinical risk.

Exploring current smartphone-based cognitive assessments in schizophrenia and bipolar disorder.

Schizophrenia and bipolar disorder are associated with cognitive deficits that contribute significantly to disability. However, traditional in-lab cognitive assessments are time-consuming and not optimized for remote administration. Recent advancements in smartphone technology enable momentary cognitive assessments in a real-world context. This brief report reviews recent research in momentary cognitive assessments in individuals with schizophrenia and bipolar disorder through reviewing mobile platforms and cognitive assessments studied. A total of 14 experimental articles were reviewed, focusing on cognitive domains including visual working memory, processing speed, executive function, verbal fluency, verbal memory, social cognition, and typing patterns. The review highlights the feasibility of remote cognitive assessment with smartphones, and provides a layout of domains studied in this context, but illustrates a low volume of current research, the need for additional studies, and the potential for innovations like digital phenotyping.

Temporal Alterations in White Matter in An App Knock-In Mouse Model of Alzheimer's Disease.

Alzheimer's disease (AD) is the most common form of dementia and results in neurodegeneration and cognitive impairment. White matter (WM) is affected in AD and has implications for neural circuitry and cognitive function. The trajectory of these changes across age, however, is still not well understood, especially at earlier stages in life. To address this, we used the AppNL-G-F/NL-G-F knock-in (APPKI) mouse model that harbors a single copy knock-in of the human amyloid precursor protein (APP) gene with three familial AD mutations. We performed in vivo diffusion tensor imaging (DTI) to study how the structural properties of the brain change across age in the context of AD. In late age APPKI mice, we observed reduced fractional anisotropy (FA), a proxy of WM integrity, in multiple brain regions, including the hippocampus, anterior commissure (AC), neocortex, and hypothalamus. At the cellular level, we observed greater numbers of oligodendrocytes in middle age (prior to observations in DTI) in both the AC, a major interhemispheric WM tract, and the hippocampus, which is involved in memory and heavily affected in AD, prior to observations in DTI. Proteomics analysis of the hippocampus also revealed altered expression of oligodendrocyte-related proteins with age and in APPKI mice. Together, these results help to improve our understanding of the development of AD pathology with age, and imply that middle age may be an important temporal window for potential therapeutic intervention.

Default Mode Network Hypoalignment of Function to Structure Correlates With Depression and Rumination.

BACKGROUND: Recent studies have begun to examine how signals in the brain correspond to the underlying white matter structure using tools from the field of graph signal processing to quantify brain function alignment to brain network topology. Here, we applied this framework for the first time toward a transdiagnostic cohort of individuals with internalizing psychopathologies, including mood and anxiety disorders, to uncover how such alignment within the default mode network (DMN) is related to depression and rumination symptoms. METHODS: Both diffusion-weighted and resting-state functional magnetic resonance imaging were obtained from participants at baseline (n = 60 patients, n = 19 healthy control participants). Patients were randomized to 12 weeks of treatment with either a selective serotonin reuptake inhibitor or cognitive behavioral therapy, and symptom scales were readministered posttreatment (n = 46 patients at follow-up). Using graph signal processing methodology, we quantified the alignment of functional signals to their underlying white matter structural networks. RESULTS: We found that signal alignment within the posterior DMN was decreased in patients with internalizing psychopathologies compared with healthy control participants and was inversely (negatively) correlated with baseline depression and rumination scales. Signal alignment within the posterior DMN was also correlated with the ratio of total within-DMN to extra-DMN functional connectivity for these regions. CONCLUSIONS: These findings are consistent with previous literature regarding pathological promiscuity of posterior DMN connectivity and provide the first graph signal processing-based analyses in a transdiagnostic cohort of patients with internalizing psychopathologies.

Smartphone-derived Virtual Keyboard Dynamics Coupled with Accelerometer Data as a Window into Understanding Brain Health: Smartphone Keyboard and Accelerometer as Window into Brain Health.

We examine the feasibility of using accelerometer data exclusively collected during typing on a custom smartphone keyboard to study whether typing dynamics are associated with daily variations in mood and cognition. As part of an ongoing digital mental health study involving mood disorders, we collected data from a well-characterized clinical sample (N = 85) and classified accelerometer data per typing session into orientation (upright vs. not) and motion (active vs. not). The mood disorder group showed lower cognitive performance despite mild symptoms (depression/mania). There were also diurnal pattern differences with respect to cognitive performance: individuals with higher cognitive performance typed faster and were less sensitive to time of day. They also exhibited more well-defined diurnal patterns in smartphone keyboard usage: they engaged with the keyboard more during the day and tapered their usage more at night compared to those with lower cognitive performance, suggesting a healthier usage of their phone.

Association Between User Interaction and Treatment Response of a Voice-Based Coach for Treating Depression and Anxiety: Secondary Analysis of a Pilot Randomized Controlled Trial.

BACKGROUND: The quality of user interaction with therapeutic tools has been positively associated with treatment response; however, no studies have investigated these relationships for voice-based digital tools. OBJECTIVE: This study evaluated the relationships between objective and subjective user interaction measures as well as treatment response on Lumen, a novel voice-based coach, delivering problem-solving treatment to patients with mild to moderate depression or anxiety or both. METHODS: In a pilot trial, 42 adults with clinically significant depression (Patient Health Questionnaire-9 [PHQ-9]) or anxiety (7-item Generalized Anxiety Disorder Scale [GAD-7]) symptoms or both received Lumen, a voice-based coach delivering 8 problem-solving treatment sessions. Objective (number of conversational breakdowns, ie, instances where a participant's voice input could not be interpreted by Lumen) and subjective user interaction measures (task-related workload, user experience, and treatment alliance) were obtained for each session. Changes in PHQ-9 and GAD-7 scores at each ensuing session after session 1 measured the treatment response. RESULTS: Participants were 38.9 (SD 12.9) years old, 28 (67%) were women, 8 (19%) were Black, 12 (29%) were Latino, 5 (12%) were Asian, and 28 (67%) had a high school or college education. Mean (SD) across sessions showed breakdowns (mean 6.5, SD 4.4 to mean 2.3, SD 1.8) decreasing over sessions, favorable task-related workload (mean 14.5, SD 5.6 to mean 17.6, SD 5.6) decreasing over sessions, neutral-to-positive user experience (mean 0.5, SD 1.4 to mean 1.1, SD 1.3), and high treatment alliance (mean 5.0, SD 1.4 to mean 5.3, SD 0.9). PHQ-9 (Ptrend=.001) and GAD-7 scores (Ptrend=.01) improved significantly over sessions. Treatment alliance correlated with improvements in PHQ-9 (Pearson r=-0.02 to -0.46) and GAD-7 (r=0.03 to -0.57) scores across sessions, whereas breakdowns and task-related workload did not. Mixed models showed that participants with higher individual mean treatment alliance had greater improvements in PHQ-9 (ß=-1.13, 95% CI -2.16 to -0.10) and GAD-7 (ß=-1.17, 95% CI -2.13 to -0.20) scores. CONCLUSIONS: The participants had fewer conversational breakdowns and largely favorable user interactions with Lumen across sessions. Conversational breakdowns were not associated with subjective user interaction measures or treatment responses, highlighting how participants adapted and effectively used Lumen. Individuals experiencing higher treatment alliance had greater improvements in depression and anxiety. Understanding treatment alliance can provide insights on improving treatment response for this new delivery modality, which provides accessibility, flexibility, comfort with disclosure, and cost-related advantages compared to conventional psychotherapy. TRIAL REGISTRATION: ClinicalTrials.gov NCT04524104; https://clinicaltrials.gov/study/NCT04524104.

Experimental Therapeutics: Opportunities and Challenges Stemming From the National Institute of Mental Health Workshop on Novel Target Discovery and Psychosocial Intervention Development.

There has been slow progress in the development of interventions that prevent and/or reduce mental-health morbidity and mortality. The National Institute of Mental Health (NIMH) launched an experimental-therapeutics initiative with the goal of accelerating the development of effective interventions. The emphasis is on interventions designed to engage a target mechanism. A target mechanism is a process (e.g., behavioral, neurobiological) proposed to underlie change in a defined clinical endpoint and through change in which an intervention exerts its effect. This article is based on discussions from an NIMH workshop conducted in February 2020 and subsequent conversations among researchers using this approach. We discuss the components of an experimental-therapeutics approach such as clinical-outcome selection, target definition and measurement, intervention design and selection, and implementation of a team-science strategy. We emphasize the important contributions of different constituencies (e.g., patients, caregivers, providers) in deriving hypotheses about novel target mechanisms. We highlight strategies for target-mechanism identification using published and hypothetical examples. We consider the decision-making dilemmas that arise with different patterns of results in purported mechanisms and clinical outcomes. We end with considerations of the practical challenges of this approach and the implications for future directions of this initiative.

Disrupted excitation-inhibition balance in cognitively normal individuals at risk of Alzheimer's disease.

Background: Sex differences impact Alzheimer's disease (AD) neuropathology, but cell-to-network level dysfunctions in the prodromal phase are unclear. Alterations in hippocampal excitation-inhibition balance (EIB) have recently been linked to early AD pathology. Objective: Examine how AD risk factors (age, APOE-ɛ4, amyloid-ß) relate to hippocampal EIB in cognitively normal males and females using connectome-level measures. Methods: Individuals from the OASIS-3 cohort (age 42-95) were studied (N = 437), with a subset aged 65+ undergoing neuropsychological testing (N = 231). Results: In absence of AD risk factors (APOE-ɛ4/Aß+), whole-brain EIB decreases with age more significantly in males than females (p = 0.021, ß = -0.007). Regression modeling including APOE-ɛ4 allele carriers (Aß-) yielded a significant positive AGE-by-APOE interaction in the right hippocampus for females only (p = 0.013, ß = 0.014), persisting with inclusion of Aß+ individuals (p = 0.012, ß = 0.014). Partial correlation analyses of neuropsychological testing showed significant associations with EIB in females: positive correlations between right hippocampal EIB with categorical fluency and whole-brain EIB with the trail-making test (p < 0.05). Conclusion: Sex differences in EIB emerge during normal aging and progresses differently with AD risk. Results suggest APOE-ɛ4 disrupts hippocampal balance more than amyloid in females. Increased excitation correlates positively with neuropsychological performance in the female group, suggesting a duality in terms of potential beneficial effects prior to cognitive impairment. This underscores the translational relevance of APOE-ɛ4 related hyperexcitation in females, potentially informing therapeutic targets or early interventions to mitigate AD progression in this vulnerable population.

Network Analysis of Behavioral Activation/Inhibition Systems and Brain Volume in Individuals With and Without Major Depressive Disorder or Social Anxiety Disorder.

BACKGROUND: Social anxiety disorder (SAD) and major depressive disorder (MDD) are characterized by behavioral abnormalities in motivational systems, namely the behavioral inhibition system (BIS) and behavioral activation system (BAS). Limited studies indicate brain volume in regions that support emotion, learning/memory, reward, and cognitive functions relate to BIS/BAS. To increase understanding of BIS/BAS, the current study used a network approach. METHODS: Patients with SAD (n = 59), patients with MDD (n = 64), and healthy control participants (n = 36) completed a BIS/BAS questionnaire and structural magnetic resonance imaging scans; volumetric regions of interest comprised cortical and limbic structures based on previous BIS/BAS studies. A Bayesian Gaussian graphical model was used for each diagnostic group, and groups were compared. Among network metrics, bridge centrality was of primary interest. Analysis of variance evaluated BIS/BAS behaviors between groups. RESULTS: Bridge centrality showed hippocampus positively related to BAS, but not to BIS, in the MDD group; no findings were observed in the SAD or control groups. Yet, network density (i.e., overall strength of relationships between variables) and degree centrality (i.e., overall relationship between one variable to all other variables) showed that cortical (e.g., precuneus, medial orbitofrontal) and subcortical (e.g., amygdala, hippocampus) regions differed between diagnostic groups. Analysis of variance results showed BAS was lower in the MDD/SAD groups compared with the control group, while BIS was higher in the SAD group relative to the MDD group, which in turn was higher than the control group. CONCLUSIONS: Preliminary findings indicate that network-level aberrations may underlie motivational abnormalities in MDD and SAD. Evidence of BIS/BAS differences builds on previous work that points to shared and distinct motivational differences in internalizing psychopathologies.

Disrupted Excitation-Inhibition Balance in Cognitively Normal Individuals at Risk of Alzheimer's Disease.

BACKGROUND: Sex differences impact Alzheimer's disease (AD) neuropathology, but cell-to-network level dysfunctions in the prodromal phase are unclear. Alterations in hippocampal excitation-inhibition balance (EIB) have recently been linked to early AD pathology. OBJECTIVE: Examine how AD risk factors (age, APOEɛ4, amyloid-ß) relate to hippocampal EIB in cognitively normal males and females using connectome-level measures. METHODS: Individuals from the OASIS-3 cohort (age 42-95) were studied (N = 437), with a subset aged 65+ undergoing neuropsychological testing (N = 231). RESULTS: In absence of AD risk factors (APOEɛ4/Aß+), whole-brain EIB decreases with age more significantly in males than females (p = 0.021, ß= -0.007). Regression modeling including APOEɛ4 allele carriers (Aß-) yielded a significant positive AGE-by-APOE interaction in the right hippocampus for females only (p = 0.013, ß= 0.014), persisting with inclusion of Aß+ individuals (p = 0.012, ß= 0.014). Partial correlation analyses of neuropsychological testing showed significant associations with EIB in females: positive correlations between right hippocampal EIB with categorical fluency and whole-brain EIB with the Trail Making Test (p < 0.05). CONCLUSIONS: Sex differences in EIB emerge during normal aging and progresses differently with AD risk. Results suggest APOEɛ4 disrupts hippocampal balance more than amyloid in females. Increased excitation correlates positively with neuropsychological performance in the female group, suggesting a duality in terms of potential beneficial effects prior to cognitive impairment. This underscores the translational relevance of APOEɛ4 related hyperexcitation in females, potentially informing therapeutic targets or early interventions to mitigate AD progression in this vulnerable population.

Development of a Bayesian multimodal model to detect biomarkers in neuroimaging studies.

In this article, we developed a Bayesian multimodal model to detect biomarkers (or neuromarkers) using resting-state functional and structural data while comparing a late-life depression group with a healthy control group. Biomarker detection helps determine a target for treatment intervention to get the optimal therapeutic benefit for treatment-resistant patients. The borrowing strength of the structural connectivity has been quantified for functional activity while detecting the biomarker. In the biomarker searching process, thousands of hypotheses are generated and tested simultaneously using our novel method to control the false discovery rate for small samples. Several existing statistical approaches, frequently used in analyzing neuroimaging data have been investigated and compared via simulation with the proposed approach to show its excellent performance. Results are illustrated with a live data set generated in a late-life depression study. The role of detected biomarkers in terms of cognitive function has been explored.