Neuroimaging Research Group

A fundamental neuroscience topic is the link between the brain’s molecular, cellular, and cytoarchitectonic properties and structural connectivity. Recent studies relate inter-regional connectivity to gene expression, but the relationship to regional cell-type distributions remains understudied. In a recent paper published in the prestigious journal Cell Reports from Dr. Ashish Raj’s Lab titled “Spatial cell-type enrichment predicts mouse brain connectivity”, examined the relationships between regional brain connectivity, gene expression data, and cell-type distributions. They utilized whole-brain mapping of neuronal and non-neuronal subtypes via the matrix inversion and subset selection algorithm, developed by then in an earlier publication, to model inter-regional connectivity as a function of regional cell-type composition with machine learning. They deployed random forest algorithms for predicting connectivity from cell-type densities, demonstrating surprisingly strong prediction accuracy of cell types in general, and particular non-neuronal cells such as oligodendrocytes. We found evidence of a strong distance dependency in the cell connectivity relationship, with layer-specific excitatory neurons contributing the most for long-range connectivity, while vascular and astroglia were salient for short-range connections. These results demonstrate a link between cell types and connectivity, providing a roadmap for examining this relationship in other species, including humans.

Dynamic hyperpolarized (HP)-¹³C MRI has enabled real-time, non-invasive assessment of Warburg-related metabolic dysregulation in glioma using a [1-¹³C] pyruvate tracer that undergoes conversion to [1-¹³C]lactate and [¹³C]bicarbonate. Using a multi-parametric ¹H/HP-¹³C imaging approach, Dr. Yan Li’s lab investigated dynamic and steady-state metabolism, together with physiological parameters, in high-grade gliomas to characterize active tumor. In a recent paper published in the journal Neuroimage Clinical titled “Multi-parametric hyperpolarized ¹³C/¹H imaging reveals Warburg-related metabolic dysfunction and associated regional heterogeneity in high-grade human gliomas”, they acquired multi-parametric ¹H/HP-¹³C MRI data from fifteen patients with progressive/treatment-naïve glioblastoma [prog/TN GBM, IDH-wildtype (n = 11)], progressive astrocytoma, IDH-mutant, grade 4 (G4A^IDH+, n = 2) and GBM manifesting treatment effects (n = 2). Regional analysis of Prog/TN GBM metabolism revealed statistically significant heterogeneity in ¹H choline-to-N-acetylaspartate index (CNI)_max, [1-¹³C]lactate, modified [1-¹³C]lactate-to-[1-¹³C]pyruvate ratio (CEL_val > NEL_val > NAWM_val); [1-¹³C]lactate-to-[¹³C]bicarbonate ratio (CEL_val > NEL_val/NAWM_val); and ¹H-lactate (CEL_val/NEL_val > NAWM_undetected). Significant associations were also found between normalized perfusion (cerebral blood volume, nCBV; peak height, nPH) and levels of [1-¹³C]pyruvate and [1-¹³C]lactate, as well as between CNI_max and levels of [1-¹³C]pyruvate, [1-¹³C]lactate and modified ratio. GBM, by comparison to G4A^IDH+, displayed lower perfusion %-recovery and modeled rate constants for [1-¹³C]pyruvate-to-[1-¹³C]lactate conversion (k_PL), and higher ¹H-lactate and [1-¹³C]pyruvate levels, while having higher nCBV, %-recovery, k_PL, [1-¹³C]pyruvate-to-[1-¹³C]lactate and modified ratios relative to treatment effects. These results indicate that GBM consistently displayed aberrant, Warburg-related metabolism and regional heterogeneity detectable by novel HP-¹³C/¹H imaging techniques.

Imaging biomarkers remain mostly unavailable for non-Alzheimer's disease neuropathological changes (non-ADNC) such as transactive response DNA-binding protein 43 (TDP-43) proteinopathy, Lewy body disease (LBD), and cerebral amyloid angiopathy (CAA). A recent publication from Dr. Duygu Tosun’s group this year in the journal Alzheimer’s and Dementia titled “Identifying individuals with non-Alzheimer's disease co-pathologies: A precision medicine approach to clinical trials in sporadic Alzheimer's disease” addressed this problem. A multilabel non-ADNC classifier using magnetic resonance imaging (MRI) signatures was developed for TDP-43, LBD, and CAA in an autopsy-confirmed cohort (N = 214). A model using demographic, genetic, clinical, MRI, and ADNC variables (amyloid positive [Aβ+] and tau+) in autopsy-confirmed participants showed accuracies of 84% for TDP-43, 81% for LBD, and 81% to 93% for CAA, outperforming reference models without MRI and ADNC biomarkers. In an ADNI cohort (296 cognitively unimpaired, 401 mild cognitive impairment, 188 dementia), Aβ and tau explained 33% to 43% of variance in cognitive decline; imputed non-ADNC explained an additional 16% to 26%. Accounting for non-ADNC decreased the required sample size to detect a 30% effect on cognitive decline by up to 28%. These results lead to a better understanding of the factors that influence cognitive decline and may lead to improvements in clinical trial design for dementia patients.

Sleep is a highly stereotyped phenomenon, requiring robust spatiotemporal coordination of neural activity. Understanding how the brain coordinates neural activity with sleep onset can provide insights into the physiological functions subserved by sleep and the pathologic phenomena associated with sleep onset. In a recent publication that is a collaboration between the laboratories of Drs. Nagarajan, Ranasinghe and Raj published in the Journal of Neuroscience titled “Cortical synchrony and information flow during transition from wakefulness to light non-rapid eye movement sleep”, they quantified whole-brain network changes in synchrony and information flow during the transition from wakefulness to light non-rapid eye movement (NREM) sleep, using magnetoencephalography imaging in a convenient sample of 14 healthy human participants (11 female; mean 63.4 y [SD 11.8]). They also performed computational modeling to infer excitatory and inhibitory properties of local neural activity. The transition from wakefulness to light NREM was identified to be encoded in spatially and temporally specific patterns of long-range synchrony. Within the delta band, there was a global increase in connectivity from wakefulness to light NREM, which was highest in fronto-parietal regions. Within the theta band, there was an increase in connectivity in fronto-parieto-occipital regions and a decrease in temporal regions from wakefulness to N1. Patterns of information flow revealed that mesial frontal regions receive hierarchically organized inputs from broad cortical regions upon sleep onset, including direct inflow from occipital regions and indirect inflow via parieto-temporal regions within the delta frequency band. Finally, biophysical neural mass modeling demonstrated changes in the anterior-to-posterior distribution of cortical excitation-to-inhibition with increased excitation-to-inhibition model parameters in anterior regions in light NREM as compared to wakefulness. Together, these findings uncover whole-brain corticocortical structure and the orchestration of local and long-range, frequency-specific cortical interactions in the sleep-wake transition.