Oscillatory Network Activity in Brain Functions and Dysfunctions

Recent experimental studies point to the notion that the brain is a complex dynamical system whose behaviors relating to brain functions and dysfunctions can be described by the physics of network phenomena. The brain consists of anatomical axonal connections among neurons and neuronal populations in various spatial scales. Neuronal interactions and synchrony of neuronal oscillations are central to normal brain functions. Breakdowns in interactions and modifications in synchronization behaviors are usual hallmarks of brain dysfunctions. Here, in this dissertation for PhD degree in physics, we report discoveries of brain oscillatory network activity from two separate studies. These studies investigated the large-scale brain activity during tactile perceptual decision-making and epileptic seizures. In the perceptual decision-making study, using scalp electroencephalography (EEG) recordings of brain potentials, we investigated how oscillatory activity functionally organizes different neocortical regions as a network during a tactile discrimination task. While undergoing EEG recordings, blindfolded healthy participants felt a linear three-dot array presented electromechanically, under computer control, and reported whether the central dot was offset to the left or right. Based on the current dipole modeling in the brain, we found that the source-level peak activity appeared in the left primary somatosensory cortex (SI), right lateral occipital complex (LOC), right posterior intraparietal sulcus (pIPS) and finally left dorsolateral prefrontal cortex (dlPFC) at 45, 130, 160 and 175 ms respectively. Spectral interdependency analysis showed that fine tactile discrimination is mediated by distinct but overlapping ~15 Hz beta and ~80 Hz gamma band large-scale oscillatory networks. The beta-network that included all four nodes was dominantly feedforward, similar to the propagation of peak cortical activity, implying its role in accumulating and maintaining relevant sensory information and mapping to action. The gamma-network activity, occurring in a recurrent loop linked SI, pIPS and dlPFC, likely carrying out attentional selection of task-relevant sensory signals. Behavioral measure of task performance was correlated with the network activity in both bands. In the study of epileptic seizures, we investigated high-frequency (\u3e 50 Hz) oscillatory network activity from intracranial EEG (IEEG) recordings of patients who were the candidates for epilepsy surgery. The traditional approach of identifying brain regions for epilepsy surgery usually referred as seizure onset zones (SOZs) has not always produced clarity on SOZs. Here, we investigated directed network activity in the frequency domain and found that the high frequency (\u3e80 Hz) network activities occur before the onset of any visible ictal activity, andcausal relationships involve the recording electrodes where clinically identifiable seizures later develop. These findings suggest that high-frequency network activities and their causal relationships can assist in precise delineation of SOZs for surgical resection

Higher Frequency Network Activity Flow Predicts Lower Frequency Node Activity in Intrinsic Low-Frequency BOLD Fluctuations

The brain remains electrically and metabolically active during resting conditions. The low-frequency oscillations (LFO) of the blood oxygen level-dependent (BOLD) signal of functional magnetic resonance imaging (fMRI) coherent across distributed brain regions are known to exhibit features of this activity. However, these intrinsic oscillations may undergo dynamic changes in time scales of seconds to minutes during resting conditions. Here, using wavelet-transform based timefrequency analysis techniques, we investigated the dynamic nature of default-mode networks from intrinsic BOLD signals recorded from participants maintaining visual fixation during resting conditions. We focused on the default-mode network consisting of the posterior cingulate cortex (PCC), the medial prefrontal cortex (mPFC), left middle temporal cortex (LMTC) and left angular gyrus (LAG). The analysis of the spectral power and causal flow patterns revealed that the intrinsic LFO undergo significant dynamic changes over time. Dividing the frequency interval 0 to 0.25 Hz of LFO into four intervals slow- 5 (0.01–0.027 Hz), slow-4 (0.027–0.073 Hz), slow-3 (0.073–0.198 Hz) and slow-2 (0.198–0.25 Hz), we further observed significant positive linear relationships of slow-4 in-out flow of network activity with slow-5 node activity, and slow-3 in-out flow of network activity with slow-4 node activity. The network activity associated with respiratory related frequency (slow- 2) was found to have no relationship with the node activity in any of the frequency intervals. We found that the net causal flow towards a node in slow-3 band was correlated with the number of fibers, obtained from diffusion tensor imaging (DTI) data, from the other nodes connecting to that node. These findings imply that so-called resting state is not ‘entirely’ at rest, the higher frequency network activity flow can predict the lower frequency node activity, and the network activity flow can reflect underlying structural connectivity

Is the Brain’s Inertia for Motor Movements Different for Acceleration and Deceleration?

The brain’s ability to synchronize movements with external cues is used daily, yet neuroscience is far from a full understanding of the brain mechanisms that facilitate and set behavioral limits on these sequential performances. This functional magnetic resonance imaging (fMRI) study was designed to help understand the neural basis of behavioral performance differences on a synchronizing movement task during increasing (acceleration) and decreasing (deceleration) metronome rates. In the MRI scanner, subjects were instructed to tap their right index finger on a response box in synchrony to visual cues presented on a display screen. The tapping rate varied either continuously or in discrete steps ranging from 0.5 Hz to 3 Hz. Subjects were able to synchronize better during continuously accelerating rhythms than in continuously or discretely decelerating rhythms. The fMRI data revealed that the precuneus was activated more during continuous deceleration than during acceleration with the hysteresis effect significant at rhythm rates above 1 Hz. From the behavioral data, two performance measures, tapping rate and synchrony index, were derived to further analyze the relative brain activity during acceleration and deceleration of rhythms. Tapping rate was associated with a greater brain activity during deceleration in the cerebellum, superior temporal gyrus and parahippocampal gyrus. Synchrony index was associated with a greater activity during the continuous acceleration phase than during the continuous deceleration or discrete acceleration phases in a distributed network of regions including the prefrontal cortex and precuneus. These results indicate that the brain’s inertia for movement is different for acceleration and deceleration, which may have implications in understanding the origin of our perceptual and behavioral limits

Comparing empirical kinship derived heritability for imaging genetics traits in the UK biobank and human connectome project

Imaging genetics analyses use neuroimaging traits as intermediate phenotypes to infer the degree of genetic contribution to brain structure and function in health and/or illness. Coefficients of relatedness (CR) summarize the degree of genetic similarity among subjects and are used to estimate the heritability – the proportion of phenotypic variance explained by genetic factors. The CR can be inferred directly from genome-wide genotype data to explain the degree of shared variation in common genetic polymorphisms (SNP-heritability) among related or unrelated subjects. We developed a central processing and graphics processing unit (CPU and GPU) accelerated Fast and Powerful Heritability Inference (FPHI) approach that linearizes likelihood calculations to overcome the ∼N2–3 computational effort dependency on sample size of classical likelihood approaches. We calculated for 60 regional and 1.3 × 105 voxel-wise traits in N = 1,206 twin and sibling participants from the Human Connectome Project (HCP) (550 M/656 F, age = 28.8 ± 3.7 years) and N = 37,432 (17,531 M/19,901 F; age = 63.7 ± 7.5 years) participants from the UK Biobank (UKBB). The FPHI estimates were in excellent agreement with heritability values calculated using Genome-wide Complex Trait Analysis software (r = 0.96 and 0.98 in HCP and UKBB sample) while significantly reducing computational (102–4 times). The regional and voxel-wise traits heritability estimates for the HCP and UKBB were likewise in excellent agreement (r = 0.63–0.76, p \u3c 10−10). In summary, the hardware-accelerated FPHI made it practical to calculate heritability values for voxel-wise neuroimaging traits, even in very large samples such as the UKBB. The patterns of additive genetic variance in neuroimaging traits measured in a large sample of related and unrelated individuals showed excellent agreement regardless of the estimation method. The code and instruction to execute these analyses are available at www.solar-eclipse-genetics.org

A White Matter Connection of Schizophrenia and Alzheimer’s Disease

Schizophrenia (SZ) is a severe psychiatric illness associated with an elevated risk for developing Alzheimer’s disease (AD). Both SZ and AD have white matter abnormalities and cognitive deficits as core disease features. We hypothesized that aging in SZ patients may be associated with the development of cerebral white matter deficit patterns similar to those observed in AD. We identified and replicated aging-related increases in the similarity between white matter deficit patterns in patients with SZ and AD. The white matter “regional vulnerability index” (RVI) for AD was significantly higher in SZ patients compared with healthy controls in both the independent discovery (Cohen’s d = 0.44, P = 1·10–5, N = 173 patients/230 control) and replication (Cohen’s d = 0.78, P = 9·10–7, N = 122 patients/64 controls) samples. The degree of overlap with the AD deficit pattern was significantly correlated with age in patients (r = .21 and .29, P \u3c .01 in discovery and replication cohorts, respectively) but not in controls. Elevated RVI-AD was significantly associated with cognitive measures in both SZ and AD. Disease and cognitive specificities were also tested in patients with mild cognitive impairment and showed intermediate overlap. SZ and AD have diverse etiologies and clinical courses; our findings suggest that white matter deficits may represent a key intersecting point for these 2 otherwise distinct diseases. Identifying mechanisms underlying this white matter deficit pattern may yield preventative and treatment targets for cognitive deficits in both SZ and AD patients

Depression, Stress and Regional Cerebral Blood Flow

Decreased cerebral blood flow (CBF) may be an important mechanism associated with depression. In this study we aimed to determine if the association of CBF and depression is dependent on current level of depression or the tendency to experience depression over time (trait depression), and if CBF is influenced by depression-related factors such as stressful life experiences and antidepressant medication use. CBF was measured in 254 participants from the Amish Connectome Project (age 18-76, 99 men and 154 women) using arterial spin labeling. All participants underwent assessment of symptoms of depression measured with the Beck Depression Inventory and Maryland Trait and State Depression scales. Individuals diagnosed with a unipolar depressive disorder had significantly lower average gray matter CBF compared to individuals with no history of depression or to individuals with a history of depression that was in remission at time of study. Trait depression was significantly associated with lower CBF, with the associations strongest in cingulate gyrus and frontal white matter. Use of antidepressant medication and more stressful life experiences were also associated with significantly lower CBF. Resting CBF in specific brain regions is associated with trait depression, experience of stressful life events, and current antidepressant use, and may provide a valuable biomarker for further studies

Brain Deficit Patterns of Metabolic Illnesses Overlap With Those for Major Depressive Disorder: A New Metric of Brain Metabolic Disease

Metabolic illnesses (MET) are detrimental to brain integrity and are common comorbidities in patients with mental illnesses, including major depressive disorder (MDD). We quantified effects of MET on standard regional brain morphometric measures from 3D brain MRI as well as diffusion MRI in a large sample of UK BioBank participants. The pattern of regional effect sizes of MET in non-psychiatric UKBB subjects was significantly correlated with the spatial profile of regional effects reported by the largest meta-analyses in MDD but not in bipolar disorder, schizophrenia or Alzheimer\u27s disease. We used a regional vulnerability index (RVI) for MET (RVI-MET) to measure individual\u27s brain similarity to the expected patterns in MET in the UK Biobank sample. Subjects with MET showed a higher effect size for RVI-MET than for any of the individual brain measures. We replicated elevation of RVI-MET in a sample of MDD participants with MET versus non-MET. RVI-MET scores were significantly correlated with the volume of white matter hyperintensities, a neurological consequence of MET and age, in both groups. Higher RVI-MET in both samples was associated with obesity, tobacco smoking and frequent alcohol use but was unrelated to antidepressant use. In summary, MET effects on the brain were regionally specific and individual similarity to the pattern was more strongly associated with MET than any regional brain structural metric. Effects of MET overlapped with the reported brain differences in MDD, likely due to higher incidence of MET, smoking and alcohol use in subjects with MDD

Brain-Wide Versus Genome-Wide Vulnerability Biomarkers for Severe Mental Illnesses

Severe mental illnesses (SMI), including major depressive (MDD), bipolar (BD), and schizophrenia spectrum (SSD) disorders have multifactorial risk factors and capturing their complex etiopathophysiology in an individual remains challenging. Regional vulnerability index (RVI) was used to measure individual\u27s brain‐wide similarity to the expected SMI patterns derived from meta‐analytical studies. It is analogous to polygenic risk scores (PRS) that measure individual\u27s similarity to genome‐wide patterns in SMI. We hypothesized that RVI is an intermediary phenotype between genome and symptoms and is sensitive to both genetic and environmental risks for SMI. UK Biobank sample of N = 17,053/19,265 M/F (age = 64.8 ± 7.4 years) and an independent sample of SSD patients and controls (N = 115/111 M/F, age = 35.2 ± 13.4) were used to test this hypothesis. UKBB participants with MDD had significantly higher RVI‐MDD (Cohen\u27s d = 0.20, p = 1 × 10−23) and PRS‐MDD (d = 0.17, p = 1 × 10−15) than nonpsychiatric controls. UKBB participants with BD and SSD showed significant elevation in the respective RVIs (d = 0.65 and 0.60; p = 3 × 10−5 and .009, respectively) and PRS (d = 0.57 and 1.34; p = .002 and .002, respectively). Elevated RVI‐SSD were replicated in an independent sample (d = 0.53, p = 5 × 10−5). RVI‐MDD and RVI‐SSD but not RVI‐BD were associated with childhood adversity (p \u3c .01). In nonpsychiatric controls, elevation in RVI and PRS were associated with lower cognitive performance (p \u3c 10−5) in six out of seven domains and showed specificity with disorder‐associated deficits. In summary, the RVI is a novel brain index for SMI and shows similar or better specificity for SMI than PRS, and together they may complement each other in the efforts to characterize the genomic to brain level risks for SMI

Brain deficit patterns of metabolic illnesses overlap with those for major depressive disorder: A new metric of brain metabolic disease

Metabolic illnesses (MET) are detrimental to brain integrity and are common comorbidities in patients with mental illnesses, including major depressive disorder (MDD). We quantified effects of MET on standard regional brain morphometric measures from 3D brain MRI as well as diffusion MRI in a large sample of UK BioBank participants. The pattern of regional effect sizes of MET in non-psychiatric UKBB subjects was significantly correlated with the spatial profile of regional effects reported by the largest meta-analyses in MDD but not in bipolar disorder, schizophrenia or Alzheimer\u27s disease. We used a regional vulnerability index (RVI) for MET (RVI-MET) to measure individual\u27s brain similarity to the expected patterns in MET in the UK Biobank sample. Subjects with MET showed a higher effect size for RVI-MET than for any of the individual brain measures. We replicated elevation of RVI-MET in a sample of MDD participants with MET versus non-MET. RVI-MET scores were significantly correlated with the volume of white matter hyperintensities, a neurological consequence of MET and age, in both groups. Higher RVI-MET in both samples was associated with obesity, tobacco smoking and frequent alcohol use but was unrelated to antidepressant use. In summary, MET effects on the brain were regionally specific and individual similarity to the pattern was more strongly associated with MET than any regional brain structural metric. Effects of MET overlapped with the reported brain differences in MDD, likely due to higher incidence of MET, smoking and alcohol use in subjects with MDD