Biology subjects

Michael, A. M.

Publications and source records attributed to Michael, A. M..

3 recordsLinked to original sources

bioRxiv2021

Alcohol Use Disrupts Age-Appropriate Cortical Thinning in Adolescence: A Data Driven Approach

ObjectiveCortical thickness changes dramatically during development and is influenced by adolescent drinking. However, previous findings have been inconsistent and limited by region-of-interest approaches that are underpowered because they do not conform to the underlying heterogeneity from the effects of alcohol. MethodsAdolescents (n=657; 12-22 years at baseline) from the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) who endorsed little to no alcohol use at baseline were assessed with structural MRI and followed longitudinally at four yearly intervals. Seven unique spatially covarying patterns of cortical thickness were obtained from the baseline scans by applying a novel data-driven method called non-negative matrix factorization (NMF). The cortical thickness maps of all participants longitudinal scans were projected onto vertex-level cortical patterns to obtain participant-specific coefficients for each pattern. Linear mixed-effects models were fit to each pattern to investigate longitudinal effects of alcohol consumption on cortical thickness. ResultsIn most NMF-derived cortical thickness patterns, the longitudinal rate of decline in no/low drinkers was similar for all age cohorts, among moderate drinkers the decline was faster in the younger cohort and slower in the older cohort, among heavy drinkers the decline was fastest in the younger cohort and slowest in the older cohort (FDR corrected p-values < 0.01). ConclusionsThe NMF method can delineate spatially coordinated patterns of cortical thickness at the vertex level that are unconstrained by anatomical features. Age-appropriate cortical thinning is more rapid in younger adolescent drinkers and slower in older adolescent drinkers.

neuroscience↗

bioRxiv2020

The effect of functionally-guided-connectivity-based rTMS on amygdala activation.

Repetitive transcranial magnetic stimulation (rTMS) has fundamentally transformed how we treat psychiatric disorders, but is still in need of innovation to optimally correct dysregulation that occurs throughout the fronto-limbic network. rTMS is often applied over the prefrontal cortex, a central node in this network, but less attention is given to subcortical areas because they lie at depths beyond the electric field penetration of rTMS. Recent studies have demonstrated that the effectiveness of rTMS is dependent on the functional connectivity between deep subcortical areas and superficial targets, indicating that leveraging such connectivity may improve dosing approaches for rTMS interventions. The current preliminary study, therefore, sought to test whether task-related, fMRI-connectivity-based rTMS could be used to modulate amygdala activation through its connectivity with the medial prefrontal cortex (mPFC). For this purpose, fMRI was collected on participants to identify a node in the mPFC that showed the strongest negative connectivity with right amygdala, as defined by psychophysiological interaction analysis. To promote long-lasting Hebbian-like effects, and potentially stronger modulation, 5Hz rTMS was then applied to this target as participants viewed frightening video-clips that engaged the fronto-limbic network. Post-rTMS fMRI results revealed promising increases in both the left mPFC and right amygdala, for active rTMS compared to sham. While these modulatory findings are promising, they differ from the a priori expectation that excitatory 5Hz rTMS over a negatively connected node would reduce amygdala activity. As such, further research is needed to better understand how connectivity influences TMS effects on distal structures, and to leverage this information to improve therapeutic applications.

neuroscience↗

bioRxiv2018

Early Brain Imaging can Predict Autism: Application of Machine Learning to a Clinical Imaging Archive

A comprehensive investigation of early brain alterations in Autism Spectrum Disorder (ASD) is critical for understanding the neuroanatomical underpinnings of autism and its early diagnosis. Most previous brain imaging studies in ASD, however, are based on children older than 6 years - well after the average age of ASD diagnosis (~46 months). In this study, we use brain magnetic resonance images that were collected as part of clinical routine from patients who were later diagnosed with ASD. Using 15 ASD subjects of age three to four years and 18 age-matched non-ASD subjects as controls, we perform comprehensive comparison of different brain morphometric features and ASD vs. non-ASD classification by Random Forest machine learning method. We find that, although total intracranial volume (TIV) of ASD was 5.5 % larger than in non-ASD, brain volumes of many other brain areas (as a percentage of TIV) were smaller in ASD and can be partly attributed to larger (>10 %) ventricles in ASD. The larger TIV in ASD was correlated to larger surface area and increased amount of cortical folding but not to cortical thickness. The white matter regions in ASD had less image intensity (predominantly in the frontal and temporal regions) suggesting myelination deficit. We achieved 95 % area under the ROC curve (AUC) for ASD vs. non-ASD classification using all brain features. When classification was performed separately for each feature type, image intensity yielded the highest predictive power (95 % AUC), followed by cortical folding index (69 %), cortical and subcortical volume (69 %), and surface area (68 %). The most important feature for classification was white matter intensity surrounding the rostral middle frontal gyrus and was lower in ASD (d = 0.77, p = 0.04). The high degree of classification success indicates that the application of machine learning methods on brain features holds promise for earlier identification of ASD. To our knowledge this is the first study to leverage a clinical imaging archive to investigate early brain markers in ASD.

bioinformatics↗