Connecting late-life depression and cognition with statistical physics based connectomics and sparse Frechet regression
(PI: Alex Leow/Yichao Wu/Liang Zhang)
1R01MH125928-01
Recently, several lines of evidence have supported that synaptic dysfunction represents one of the earliest brain changes in Alzheimer’s disease (AD), leading to hyper-excitation in neuronal circuits. However, network changes related to age, sex and other risk factors such as the apolipoprotein E (APOE) ε4 allele tend to overlap with disease neuropathology, increasing the difficulty of separating disease-specific alterations from those related to normal aging trajectories in males and females (women comprise two thirds of all persons diagnosed with AD dementia, while female ε4 allele carriers are four times more likely to develop AD than men). Further compounding the challenges is the potential for psychiatric conditions to influence these relationships. Specifically, late life depression (LLD) has been proposed as a significant contributor to accelerated cognitive decline and progression to dementia. While it remains unclear which neurobiological aspects of LLD represent pathognomonic features, versus co- occurring aspects of AD, determining their impact on functional outcomes is a significant opportunity to disentangle the relationship between depression and neurodegenerative processes in late life. We will use multi-modal connectomics to analyze excitation-inhibition balance (E-I balance) in the well-characterized ADNI and ADNI-D samples to elucidate the relationship between late- life depression and neurodegeneration. Our pipeline will be based on a novel resting-state structural connectomics (rs-SC) approach that yields a hyperexcitation indicator (HI). Previously, in a group of cognitively normal APOE-ε4 carriers and age/gender matched non- carriers we demonstrated a sex-by-age-by-genotype interaction, with significant hyperexcitation with increasing age only observable in women, but not in men. In particular, results supported that hyperexcitation in female carriers began to exhibit at age 50 in the default mode network (DMN). Further, the degree of hyperexcitation was shown to be related to compensatory recruitment of neuronal resources during a spatial learning memory task (virtual Morris water maze task). Motivated by this pilot study, we will examine the links between mood (late-life depression) and subsequent cognitive decline and development of dementia in the context of synaptic dysfunction.
CRCNS: Investigating Brain Dynamics Through The Lens of Statistical Mechanics
(PI: Liang Zhan/Alex Leow)
1R01AG071243-01
Synaptic dysfunction has been hypothesized to be one of the earliest brain changes in Alzheimer’s disease (AD), leading to hyper-excitation in neuronal circuits. However, network changes related to age and sex tend to overlap with disease neuropathology, increasing the difficulty of separating disease-specific alterations from those related to normal aging trajectories in males and females. Indeed, AD disproportionately affects women, who comprise two thirds of all persons diagnosed with AD dementia. Leveraging resting state fMRI connectome and diffusion MRI-derived structural connectome, we will use a novel hybrid resting-state structural connectome (rs-SC) to study excitation-inhibition balance. Recently, using a group of cognitively normal APOE-ε4 carriers and age/gender matched non-carriers we demonstrated a sex-by-age-by-phenotype interaction, with significant hyperexcitation with increasing age only observable in women, but not in men. Further, hyperexcitation in female carriers began to exhibit at age 50 in the anterior cingulate, parahippocampal gyrus and temporal lobe regions, and the degree of hyperexcitation is linked to compensatory recruitment of neuronal resources during a spatial learning memory task. In this proposal, we will characterize 1) sex-specific normative trajectories of excitation-inhibition balance using the Human Connectome Project (HCP) data, and 2) altered excitation-inhibition balance in abnormal aging using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data, as well as 3) further test and validate our hyperexcitation framework in longitudinal mouse models of AD. RELEVANCE (See instructions): In this proposal, we will develop novel computational tools to characterize hyper-excitation patterns in aging and Alzheimer's Disease and validate our hyperexcitation framework on human data (ADNI and HCP) as well as longitudinal mouse models of AD. This will significantly improve our understanding of AD and potentially accelerate the discovery of more robust non-invasive imaging biomarkers of AD.
Study of a PST-Trained Voice-Enabled Artificial Intelligence Counselor (SPEAC) For Adults with Emotional Distress
(PI: Jun Ma/Olusola Ajilore)
1R61MH119237-01A1
BACKGROUND: Depression and anxiety are the leading causes of disability and lost productivity, and are often underdiagnosed and undertreated owing to access, cost, and stigma barriers. Novel and scalable psychotherapies are urgently needed. Advances in artificial intelligence (AI) offer a transformative opportunity to develop intelligent voice assistants as virtual health agents accessible on personal devices. Meanwhile, major advances in human neuroscience have fueled a paradigm shift to study brain mechanisms underlying behavioral health interventions. OBJECTIVES: Leveraging our collaborative team’s transdisciplinary expertise in these emerging areas, we will develop and rigorously test a novel voice-enabled, AI virtual agent named Lumen, trained on Problem Solving Therapy (PST), for patients with moderate, untreated depressive and/or anxiety symptoms. We will investigate the effect of Lumen on engagement of a priori neural targets—amygdala for emotional reactivity and dorsal lateral prefrontal cortex (DLPFC) for cognitive control—as putative mechanisms. DESIGN/ METHODS: The project has 2 phases. In the R61 phase (years 1-2), we will further develop Lumen building on the current prototype and conduct iterative user-centered design evaluations that include focus groups, scenario-based clinician evaluations, and a formative user study with 20 participants. We will pilot test Lumen in a 2-arm randomized clinical trial (RCT, Study 1), with 60 participants with depression and/or anxiety randomized in a 2:1 ratio to receive PST with Lumen (n=40) on a secure study iPad or be on a waitlist (n=20). At weeks 0 and 14, participants will complete functional magnetic resonance imaging (fMRI) to assess neural target engagement as well as validated surveys of patient-reported outcomes (e.g., depressive and anxiety symptoms, functioning, quality of life). In addition, they will complete naturalistic end-of-day assessments of mood, stress, appraisal and coping for 7 days every 2 weeks. If the Go milestone criteria are met, the R33 phase (years 3-5) will include a 3-arm RCT (Study 2) with 200 new participants randomized in a 2:1:1 ratio to 1 of 3 arms: Lumen (n=100), waitlist control (n=50), and in-person PST as active control (n=50). Participants will complete baseline and follow-up assessments using a refined measurement protocol based on Study 1. SPECIFIC AIMS: R61 aims are to (1) establish the functionality, usability, and treatment fidelity of Lumen; and (2) demonstrate feasibility, acceptability, and neural target engagement according to pre-specified Go milestone criteria. R33 aims are to (1) confirm neural target engagement by a superiority test (primary) comparing the Lumen and waitlist control arms and a noninferiority test (secondary) comparing the Lumen and in-person PST arms; and (2) examine the relationships of target engagement to outcomes. The results will provide the basis for future confirmatory efficacy testing of Lumen. IMPACT: This project’s public health impact lies in that a mechanistically tested, PST-trained AI agent could bring proven psychotherapy to people with depression/anxiety who do not seek professional help or who desire more personalized, connected care.
Unobtrusive Monitoring of Affective Symptoms and Cognition Using Keyboard Dynamics (UnMASCK)
(PI: Olusola Ajilore/Alex Leow)
1R01MH120168-01A1
Cognitive dysfunction in the context of mood disorders has been associated with poorer treatment response and is a key risk factor for recurrence after remission. Thus, accurate, objective assessment of cognitive function in patients with mood disorders is of paramount clinical utility. The proposed study will use a novel smartphone technology (“BiAffect”) that uses typing dynamics and motor kinematics to unobtrusively, securely, and passively monitor cognitive function in a transdiagnostic sample of participants with mood disorders (unipolar depression, bipolar disorder type I/II, dysthymia). The core technology of BiAffect is a custom-built smartphone virtual keyboard that replaces the native default keyboard, allowing the collection of real-time data of real and potential clinical relevance while individuals interact with their device as usual within their natural environment. BiAffect will be used in the sample to predict 1) altered brain network properties associated with cognitive dysfunction in mood disorders and 2) prospective changes in clinical mood symptoms in a transdiagnostic sample of participants with mood disorders.
Recurrence Markers, Cognitive Burden and Neurobiological homeostasis in Late-Life Depression (REMBRANDT)
(PI: Olusola Ajilore)
1R01MH121384-01
Repeated major depressive episodes are particularly problematic for older adults who have a more brittle recovery than younger adults. Our data show that, despite antidepressant treatment, almost 60% of remitted older adults experience recurrence within four years. Beyond simply relying on past history and reported current stress, it is unclear what neurobiological factors are prospectively associated with recurrence risk, when these factors trigger recurrence, and how they contribute to the high rates of cognitive impairment observed in late-life depression (LLD). Using a model of network homeostasis, we posit that depressive episodes are characterized by disrupted homeostasis in key neural networks involved in affect regulation and cognitive function. Our preliminary data indicate that treatment non-remitters have residual functional network alterations and high network instability (higher fluctuations in temporal signal-to-noise ratio). We hypothesize that remitters with residual functional network alterations and greater instability remain at high risk of recurrence with subsequent stress exposure. This disequilibrium contributes to subsyndromal symptoms followed by full recurrence. These processes may also contribute to the higher rate of cognitive impairment and decline observed in LLD. Our groups have reported elevated rates of cognitive decline in remitted LLD and an association of recurrence with accelerated brain aging. We hypothesize that greater neural reactivity to stress may accelerate brain aging and cognitive decline and that deficits/variability in performance on tasks dependent on ECN may serve as markers of network alterations and signal increased recurrence risk. The goals of this study are to A) identify neurobiological factors that predict recurrence risk, and B) examine how cognitive performance changes are both influenced by these same neurobiological factors and also predict recurrence risk. Our approach is to conduct a three-site, two-year longitudinal study of remitted LLD and never-depressed elders. Every 8 months we will conduct laboratory assessments, including clinical, cognitive and neuroimaging assessments and an in-scanner stress paradigm, along with burst ecological momentary assessments (EMA) of mood variability, stress exposure, cognitive performance, and passive actigraphy. As an exploratory goal, we will examine whether continuous ecological monitoring of mood and activity can provide early detection of recurrence. A subgroup will be continuously monitored by EMA and actigraphy for state shifts (persistent worsening) or variance shifts (increased variability) in symptom severity. When shifts in mood symptoms are identified, they will engage in ad-hoc clinical and neuroimaging testing. Results from this study may be translated in clinical practice through the future development of easy-to-use platforms (e.g. apps) that signal to clinicians increased risk of impending recurrence, thus allowing for swift therapeutic intervention.
Highly-sensitive imaging markers for early detection of Alzheimer's Disease using multi-view connectomics
(PI: Alex Leow)
1R21AG056782-01
Alzheimer's disease (AD) is the most common form of dementia, with the number of affected Americans expected to reach 13.4 million by the year 2050. While it is well known that AD leads to progressive neuronal death, the exact mechanism of AD remains elusive. Currently, a definitive diagnosis can only be reached by autopsy or brain biopsy, and the neurodegenerative processes in two AD patients can follow very different courses. Further, treatment options for AD remain limited, let alone cure. For this reason, non-invasive neuroimaging has been extensively investigated in the hope that it may provide more sensitive markers for screening and early detection of AD. Yet, despite the amount of resources devoted to AD imaging research, CSF Tau and A?42 continue to outperform any non-invasive imaging markers. Multimodal connectomics, including functional and structural connectome (derived from fMRI and diffusion MRI respectively), has the potential to gain system-level structure- function insights into the mechanisms of AD and thus offers a novel platform for developing new diagnostic strategies. Despite a number of interesting connectome findings in recent years, few of these connectome results have been replicated independently or proven clinically relevant, which can be partially explained by the sensitivity to parameter settings during preprocessing and connectome construction, such as the choice of brain parcellation and the type of fMRI time series correlations (full versus partial) or tractography (deterministic or probabilistic). Moreover, conventional connectome approaches usually focus on scalar summary statistics (e.g., nodal or edge-wise measures) using linear statistical techniques, which fit at each node (or edge) independent of other nodes (or edges) and thus discard important informative graph structure. Instead, this proposal will develop a multi-view connectome framework that homogenizes multiple instances of stable and reproducible high-level connectome properties across modalities and across spatiotemporal scales. This framework will be applied and cross-validated using two independent AD cohorts (Alzheimer's Disease Neuroimaging Initiative or ADNI and Wisconsin Alzheimer?s Disease Research Center cohort or Wisconsin ADRC). The identified connectome features can serve as the potential non-invasive markers for guiding the AD diagnosis.
Improving white matter integrity with thyroid hormone
(PI: Olusola Ajilore)
5R21NS095723-02
The ability to promote and support remyelination has wide-ranging implications for a number of neuropsychiatric conditions from multiple sclerosis to major depression. Pre-clinical evidence has demonstrated that thyroid hormone treatment, in the form of triiodothyronine (T3) or tetraiodothyronine (T4), can promote and support remyelination by increasing myelin basic protein mRNA and protein, oligodendrocyte proliferation and maturation, and fractional anisotropy (a diffusion imaging measure of white matter integrity). Pilot data from our own studies suggest that baseline thyroid status is correlated with the integrity of white matter tracts associated with major depression. To date, the impact of thyroid hormone administration on white matter tracts has not been studied in vivo in adult humans. The purpose of the proposed pilot study is to examine changes in white matter tract integrity using high angular diffusion imaging and multi-component relaxometry in a population of subjects clinically indicated to receive thyroid hormone for hypothyroidism. We will scan patients with hypothyroidism at the initiation of treatment and at three and six months after starting thyroid hormone treatment. We will also administer scales assessing mood and cognition which have been shown to correlate with white matter integrity. We hypothesize that thyroid hormone treatment will be associated with an increase in fractional anisotropy, a decrease in radial diffusivity, and an increase in the myelin water fraction (markers of improved myelination) that will correlate with improvements in cognition and mood ratings. If successful, this will be the first demonstration of improved white matter integrity with thyroid hormone replacement and pave the way for therapies designed to restore structural brain connectivity