Biology subjects

Fonzo, G. A.

Publications and source records attributed to Fonzo, G. A..

9 recordsLinked to original sources

bioRxiv2025

Deep Learning of Brain-Behavior Dimensions Identifies Transdiagnostic Biotypes in Youth with ADHD and Anxiety Disorders

Attention-deficit/hyperactivity disorder and anxiety disorders are highly prevalent in youth and are characterized by substantial heterogeneity and frequent co-occurrence. This transdiagnostic complexity challenges conventional diagnostic frameworks that rely on symptom-based categories, which often obscure underlying dimensional and neurobiological mechanisms and offer limited neurobiological specificity. To address these issues, we developed a deep learning-based brain-behavior modeling framework that integrates clinically salient functional connectivity with cognitive and behavioral measures to identify interpretable dimensions and biologically grounded subtypes (biotypes). We applied our model to the Adolescent Brain Cognitive Development (ABCD) dataset comprising 3,508 children aged 9-11 years and revealed two reproducible brain-behavior dimensions that captured variation in cognitive control and emotion-attention regulation. These dimensions further yielded three distinct biotypes, each exhibiting unique symptom profiles and distinct brain development. We tested the robustness and generalizability of the dimensions and corresponding biotypes in an independent cohort of 224 age-matched participants from the Healthy Brain Network (HBN) and documented their early expression before symptom onset during adolescence. These findings highlight the utility of brain-behavior dimensions for elucidating heterogeneous psychiatric presentations and advance a biologically grounded framework for early classification and potential clinical translation in youth mental health.

neuroscience↗

bioRxiv2025

Early Brain Functional Connectivity Changes Induced by Antidepressants and Placebo

Major depressive disorder (MDD) is a common and debilitating condition with limited treatment precision. While brain imaging has linked neural features to MDD diagnosis and treatment response, the underlying circuits and their early modulation by treatment remain unclear. To examine changes in functional connectivity within the first two weeks of treatment, we analyzed two independent cohorts of MDD patients receiving antidepressants or placebo. Across patients, a visual-precuneus-thalamus network showed increased functional connectivity regardless of treatment arm or clinical outcome. Placebo response involved attention and striatal systems, while drug-specific effects were localized to the amygdala, mid-cingulate, orbitofrontal cortex, and cerebellum, emerging only in a subset of medicated patients. Notably, the responses of those without drug-specific changes can be predicted with a placebo response prediction model. These early functional connectivity changes reveal common and distinct mechanisms of treatment effects, offering insights that could inform more personalized interventions for MDD.

neuroscience↗

bioRxiv2025

Functional Connectome of Superagers Reveals Early Markers of Resilience and Vulnerability to Alzheimers Disease

As populations age, identifying the neurobiological basis of cognitive resilience is critical for delaying or preventing Alzheimers disease (AD). While most older adults experience memory decline, a subset known as superagers (SA) maintains youthful memory into late life, offering a unique window into protective mechanisms against neurodegeneration. Here, we identified a functional connectivity (FC) signature, termed Alzheimers-resilient connectome (ARC), that robustly differentiates SA from age-matched patients with AD. Using resting-state fMRI in a discovery cohort (N = 290), we identified ARC derived from machine learning classifiers that distinguished SA from AD with high accuracy (AUC = 0.85), and validated the replicability of the ARC in an independent replication cohort (N = 143). ARC involved prefrontal, temporal and insular networks and was strongly associated with brain age. When applied to cognitively unimpaired (CU) adults (discovery cohort: N = 818 and replication cohort: N = 497), ARC-based subtyping revealed SA-like and AD-like subgroups with similar baseline cognitive performance but markedly divergent longitudinal trajectories. SA-like CU individuals showed slower cognitive decline, reduced amyloid-{beta} accumulation, and lower risk of conversion to mild cognitive impairment and AD, reinforcing the ARC signature as a potential early indicator of resilience. Genome-wide association analysis identified CLYBL and FRMD6 as novel genetic modulators associated with these divergent aging phenotypes. Together, our findings position ARC as a sensitive and generalizable biomarker of resilience, enabling early risk stratification and precision prevention for AD.

neuroscience↗

bioRxiv2025

Enhanced Episodic Memory Following Putative Inhibition of the Amygdala via Transcranial Low-Intensity Focused Ultrasound

The amygdala is considered crucial to the formation of emotional episodic memories, but causal evidence in humans is limited due to challenges in non-invasive neuromodulation of deep brain structures. In a double-blind, sham-controlled, repeated measures study, we examined whether transcranial low-intensity focused ultrasound (tFUS) targeting the left amygdala prior to the encoding of emotional and neutral pictures impacted memory for these pictures 24 hours later. We used a putative inhibitory tFUS protocol shown to attenuate amygdala blood-oxygenation- level-dependent signal, thus testing the hypothesis that pre-encoding amygdala inhibition diminishes emotional memory. Surprisingly, active vs. sham sonication enhanced multiple measures of neutral and emotional memory across two memory tests. A secondary test of amygdala function found that active sonication enhanced fear recognition in faces. Computational modeling further supported these results. These findings motivate a novel conceptualization of the amygdalas role in emotional episodic memory. Rather than enhancing memory via amplification of salient stimuli, the amygdala may instead act as a filter that attenuates the maintenance of non-salient stimuli in long-term memory. Finally, the potential to enhance memory serves as an impetus to test tFUS of the amygdala in disorders such as depression and posttraumatic disorder that exhibit comorbid hyperreactive amygdalae and memory impairments.

neuroscience↗

bioRxiv2024

Elucidating the neuropathological and molecular heterogeneity of amyloid-beta and tau in Alzheimer's disease through machine learning and transcriptomic integration

Discerning functional brain network variations related to neuropathological aggregates in Alzheimers disease (AD), including amyloid-{beta} (A{beta}) and phosphorylated tau (p-tau), is crucial for understanding their link to cognitive decline and underlying molecular mechanisms. However, these variations are often confounded by normal aging-related changes, complicating interpretation. To address this challenge, we first defined Alzheimers continuum cases (A{beta} positive (A+), n = 129) and normal elderly (A{beta} negative (A-), n = 160) using cerebral spinal fluid amyloid levels, and then applied a novel deep learning approach to resting-state connectivity using functional magnetic resonance imaging (fMRI) of the 289 subjects to disentangle A+-specific dimensions in brain network alterations from those shared with A- individuals. The identified A+-specific dimensions were further refined to predict individual A{beta} and p-tau levels separately. We observed that resulting brain signatures, defined from A+-specific dimensions for predicting these two CSF biomarkers, were both attributed to the right superior temporal and anterior cingulate cortices and associated with attention and memory domains. When linking the brain signatures to gene expression data from a public transcriptomic atlas, we found that the brain signatures were associated with molecular pathways involving synaptic dysfunction and disruptions in pathways containing activity of excitatory neurons, astrocytes, and microglia. For A--shared dimensions, the A{beta}-linked brain signature involved the left fusiform and right middle cingulate cortices, correlating with the language cognitive measurement and language-related molecular pathways. The p-tau-linked signature predominantly involved the right insula and inferior temporal cortices, correlating with the aging-related molecular pathways. Collectively, our findings provided new insights in understanding of Alzheimers continuum pathological biomarkers.

neuroscience↗

bioRxiv2024

Contrastive functional connectivity defines neurophysiology-informed symptom dimensions in major depression

BackgroundMajor depressive disorder (MDD) is a prevalent psychiatric disorder characterized by substantial clinical and neurobiological heterogeneity. Conventional studies that solely focus on clinical symptoms or neuroimaging metrics often fail to capture the intricate relationship between these modalities, limiting their ability to disentangle the complexity in MDD. Moreover, patient neuroimaging data typically contains normal sources of variance shared with healthy controls, which can obscure disorder-specific variance and complicate the delineation of disease heterogeneity. MethodsWe employed contrastive principal component analysis to extract disorder-specific variations in fMRI-based resting-state functional connectivity (RSFC) by contrasting MDD patients (N=233) with age-matched healthy controls (N=285). We then applied sparse canonical correlation analysis to identify latent dimensions in the disorder variations by linking the extracted contrastive connectivity features to clinical symptoms in MDD patients. ResultsTwo significant and generalizable dimensions linking distinct brain circuits and clinical profiles were discovered. The first dimension, associated with an apparent "internalizing-externalizing" symptom dimension, was characterized by self-connections within the visual network and also associated with choice reaction times of cognitive tasks. The second dimension, associated with personality facets such as extraversion and conscientiousness typically inversely associated with depression symptoms, is primarily driven by self-connections within the dorsal attention network. This "depression-protective personality" dimension is also associated with multiple cognitive task performances related to psychomotor slowing and cognitive control. ConclusionsOur contrastive RSFC-based dimensional approach offers a new avenue to dissect clinical heterogeneity underlying MDD. By identifying two stable, neurophysiology-informed symptom dimensions in MDD patients, our findings may enhance disease mechanism insights and facilitate precision phenotyping, thus advancing the development of targeted therapeutics for precision mental health. Trial RegistrationEstablishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC), NCT#01407094

neuroscience↗

bioRxiv2024

Delineating Transdiagnostic Subtypes in Neurodevelopmental Disorders via Contrastive Graph Machine Learning of Brain Connectivity Patterns

Neurodevelopmental disorders, such as Attention Deficit/Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD), are characterized by comorbidity and heterogeneity. Identifying distinct subtypes within these disorders can illuminate the underlying neurobiological and clinical characteristics, paving the way for more tailored treatments. We adopted a novel transdiagnostic approach across ADHD and ASD, using cutting-edge contrastive graph machine learning to determine subtypes based on brain network connectivity as revealed by resting-state functional magnetic resonance imaging. Our approach identified two generalizable subtypes characterized by robust and distinct functional connectivity patterns, prominently within the frontoparietal control network and the somatomotor network. These subtypes exhibited pronounced differences in major cognitive and behavioural measures. We further demonstrated the generalizability of these subtypes using data collected from independent study sites. Our data-driven approach provides a novel solution for parsing biological heterogeneity in neurodevelopmental disorders.

neuroscience↗

bioRxiv2023

Defining Dementia Subtypes Through Neuropsychiatric Symptom-Linked Brain Connectivity Patterns

BACKGROUNDDementia is highly heterogeneous, with pronounced individual differences in neuropsychiatric symptoms (NPS) and neuroimaging findings. Understanding the heterogeneity of NPS and associated brain abnormalities is essential for effective management and treatment of dementia. METHODSUsing large-scale neuroimaging data from the Open Access Series of Imaging Studies (OASIS-3), we conducted a multivariate sparse canonical correlation analysis to identify functional connectivity-informed symptom dimensions. Subsequently, we performed a clustering analysis on the obtained latent connectivity profiles to reveal neurophysiological subtypes and examined differences in abnormal connectivity and phenotypic profiles between subtypes. RESULTSWe identified two reliable neuropsychiatric subsyndromes - behavioral and anxiety in the connectivity-NPS linked latent space. The behavioral subsyndrome was characterized by the connections predominantly involving the default mode and somatomotor networks and neuropsychiatric symptoms involving nighttime behavior disturbance, agitation, and apathy. The anxiety subsyndrome was mainly contributed by connections involving the visual network and the anxiety neuropsychiatric symptom. By clustering individuals along these two subsyndromes-linked connectivity latent features, we uncovered three subtypes encompassing both dementia patients and healthy controls. Dementia in one subtype exhibited similar brain connectivity and cognitive-behavior patterns to healthy individuals. However, dementia in the other two subtypes showed different dysfunctional connectivity profiles involving the default mode, frontoparietal control, somatomotor, and ventral attention networks, compared to healthy individuals. These dysfunctional connectivity patterns were associated with differences in baseline dementia severity and longitudinal progression of cognitive impairment and behavioral dysfunction. CONCLUSIONSOur findings shed valuable insights into disentangling the neuropsychiatric and brain functional heterogeneity of dementia, offering a promising avenue to improve clinical management and facilitate the development of timely and targeted interventions for dementia patients.

neuroscience↗

bioRxiv2023

Dissecting Symptom-linked Dimensions of Resting-State Electroencephalographic Functional Connectivity in Autism with Contrastive Learning

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder characterized by social interaction deficits, communication difficulties, and restricted/repetitive behaviors or fixated interests. Despite its high prevalence, development of effective therapy for ASD is hindered by its symptomatic and neurophysiological heterogeneities. To collectively dissect the ASD heterogeneity in neurophysiology and symptoms, we develop a new analytical framework combining contrastive learning and sparse canonical correlation analysis to identify resting-state EEG connectivity dimensions linked to ASD behavioral symptoms within 392 ASD samples. Two dimensions are successfully identified, showing significant correlations with social/communication deficits (r = 0.70) and restricted/repetitive behaviors (r = 0.45), respectively. We confirm the robustness of these dimensions through cross-validation and further demonstrate their generalizability using an independent dataset of 223 ASD samples. Our results reveal that the right inferior parietal lobe is the core region displaying EEG activity associated with restricted/repetitive behaviors, and functional connectivity between the left angular gyrus and the right middle temporal gyrus is a promising biomarker of social/communication deficits. Overall, these findings provide a promising avenue to parse ASD heterogeneity with high clinical translatability, paving the way for treatment development and precision medicine for ASD.

neuroscience↗