Brain Connectivity : Neuronal Damage in COVID-19

Genotypic Differences in Networks Supporting Regional Predictors of Speech Rate in Spinocerebellar Ataxia: Preliminary Observations (https://doi.org/10.1089/brain.2020.0972)

Disordered speech production is a common characteristic of the spinocerebellar ataxias (SCAs). Although dysarthric features differ across SCAs, a previous analysis revealed that a combination of regional cerebral blood flow (rCBF) in the left inferior frontal region and the right caudate predicted syllable rate, a pattern reported in normal speakers. In this study, John J. Sidtis and Christopher M. Gomez examined the relationship between primary predictor brain regions and other areas of the brain in three SCA groups. Speech and rCBF data from 9 SCA1, 8 SCA5, and 5 SCA6 individuals were analyzed. Partial correlations were used to identify brain regions associated with the primary predictors.

They have demonstrated that secondary networks differed across SCA genotypes. SCA1 and SCA6 demonstrated both positive and negative associations between primary and secondary areas, whereas the associations in the SCA5 genotype were only positive. The SCA5 associations were also largely bilaterally symmetrical. Both SCA1 and SCA5 demonstrated secondary associations with the right caudate, whereas the SCA6 group had no such associations.

This study demonstrates that although the primary predictors of speech rate in the brain are shared in normal speakers and three genotypes of ataxia, the genotypes differ from each other in broader activity patterns associated with the primary predictors. One implication is that although basic neural circuitry may remain functional for some period of time in progressive neurological disorders, abnormal relationships may exist in the broader neurological context in which they operate.

Detecting Task-Dependent FC in Group Iterative Multiple Model Estimation with Person-Specific Hemodynamic Response Functions (https://doi.org/10.1089/brain.2020.0864)

Group iterative multiple model estimation (GIMME) has proven to be a reliable data-driven method to arrive at FC maps that represent associations between brain regions across time in groups and individuals. However, to date, GIMME has not been able to model time-varying task-related effects. This article introduces hemodynamic response function (HRF)-GIMME, an extension of GIMME that enables the modeling of the direct and modulatory effects of a task on functional MRI (fMRI) data collected using event-related designs. Critically, HRF-GIMME incorporates person-specific modeling of the HRF to accommodate known variability in onset delay and shape.

In this study by KA Duffy and KM Gates, after an introduction of the technical aspects of HRF-GIMME, the performance of HRF-GIMME is evaluated through both a simulation study and application to empirical data. The simulation study assesses the sensitivity and specificity of HRF-GIMME using data simulated from one slow and two rapid event-related designs, and HRF-GIMME is then applied to two empirical data sets from similar designs to evaluate performance in recovering known neural circuitry.

HRF-GIMME showed high sensitivity and specificity across all simulated conditions, and performed well in the recovery of expected relationships between convolved task vectors and brain regions in both simulated and empirical data, particularly for the slow event-related design.

These results indicate that HRF-GIMME is a powerful new tool for obtaining directed FC maps of intrinsic and task-related connections that is able to uncover what is common across the sample as well as crucial individual-level path connections and estimates.

Relationship Between Dynamic Blood-Oxygen-Level-Dependent Activity and Functional Network Connectivity: Characterization of Schizophrenia (https://doi.org/10.1089/brain.2020.0815)

Studies of the dynamics of human brain using fMRI have enabled the identification of unique functional network connectivity (FNC) states and provided new insights into mental disorders. There is evidence showing that both blood-oxygen-level-dependent (BOLD) activity, which is captured by fMRI, and FNC are related to mental and cognitive processes. However, few studies have evaluated the inter-relationships of these two domains of function. Moreover, identification of subgroups of schizophrenia (SZ) has gained significant clinical importance due to a need to study the heterogeneity of SZ. In this study, Qunfang Long and Tulay Adali propose a novel use of adaptively constrained independent vector analysis (acIVA) to effectively capture the temporal and spatial properties of dynamic BOLD activity (dBA), and efficiently quantify the spatial property of dBA (sdBA). They also propose to incorporate dBA into the study of brain dynamics to gain insight into activity–connectivity coevolution patterns.

The authors designed a simulation study to verify the effectiveness of acIVA and apply acIVA to the dynamic study of resting-state fMRI (rsfMRI) data collected from individuals with SZ and healthy controls (HCs) to investigate the relationship between dBA and dynamic FNC (dFNC). The simulation study demonstrates that acIVA accurately captures the spatial variability and provides an efficient quantification of sdBA. The fMRI analysis yields synchronized sdBA-temporal property of dBA (tdBA) patterns and shows that the dBA and dFNC are significantly correlated in the spatial domain. Using these dynamic features, they identify subgroups of SZ with significant differences in terms of their clinical symptoms. They find that brain function is less efficiently organized in SZs compared with HCs since there are less synchronized sdBA-tdBA patterns in SZs, and SZs prefer a component that merges multiple brain regions.

Corpus Callosum Diffusion Anisotropy and Hemispheric Lateralization of Language in Patients with Brain Arteriovenous Malformations (https://doi.org/10.1089/brain.2020.0853)

The corpus callosum (CC) plays a key role in mediating interhemispheric connectivity and developing functional hemispheric asymmetries. In this study, Maogui Li and Shuo Wang investigated the changes in CC microstructure accompanying interhemispheric language reorganization in patients with brain arteriovenous malformations (AVMs).

Patients with an unruptured AVM located in anatomically defined language areas underwent fMRI and diffusion tensor imaging. Hemispheric dominance of Broca's area (BA) and Wernicke's area (WA) was assessed separately. Right-sided or bilateral language dominance was classified as atypical lateralization. The CC was segmented into five subregions, and the mean fractional anisotropy (FA) was extracted. The relationship between callosal FA and language lateralization patterns was statistically analyzed.

The authors observed atypical language lateralization in 39.0% patients. Patients with atypical lateralization exhibited significantly higher mean FA values in the total CC (p = 0.002) and the anterior (p = 0.047), midanterior (p = 0.001) and midposterior (p = 0.043) subregions. Significant interaction effects of BA and WA lateralization were found for FA values in the total CC (p = 0.005) and the midanterior subregion (p = 0.004).

These results indicate that AVM patients with atypical language lateralization exhibit higher callosal FA values, reflecting greater interhemispheric connectivity.

Seizure Frequency Is Associated with Effective Connectivity of the Hippocampal–Diencephalic–Cingulate Model in Epilepsy with Unilateral Mesial Temporal Sclerosis (https://doi.org/10.1089/brain.2020.0835)

Temporal lobe epilepsy (TLE) with mesial temporal sclerosis (MTS) is a common intractable epilepsy. To seek neural correlates of seizure recurrence, Yao-Chia Shih and Wen-Yih Isaac Tseng along with their colleagues investigated aberrant intrinsic effective connectivity (iEC) in TLE with unilateral MTS and their associations with seizure frequency. Patients with unilateral MTS (left/right MTS = 14/16) and age-matched HCs underwent rsfMRI on a 3-Tesla MRI system. The structural equation modeling was employed to estimate the iEC of the three candidate epilepsy models, including the Papez circuit, hippocampal–diencephalic–cingulate (HDC) model, and simplified HDC model. After comparing the performance of model fitting, the best model was selected to compare iEC among the study groups. Linear regression analysis was performed to associate abnormal iEC with seizure frequency.

The simplified HDC model was the best model to estimate iEC across the three study groups (p < 0.05), and it comprised the 26 interconnected pathways between the mesial temporal lobe, thalamus, and cingulate cortices. The linear regression analysis revealed a significant relationship between the shared iEC alterations in both patient groups and seizure frequency, including the three paths of mammillary body → bilateral anterior thalamic nuclei and right hippocampus → mammillary body. Their findings provide new insights into neurophysiological significance relevant to seizure recurrence. Aberrant iEC on the neural paths connected to the mammillary body can be a potential imaging marker, aiding the therapeutic management in TLE with unilateral MTS.

Dynamic Configuration of Coactive Micropatterns in the Default Mode Network During Wakefulness and Sleep (https://doi.org/10.1089/brain.2020.0827)

The default mode network (DMN) is a prominent intrinsic network that is observable in many mammalian brains. However, few studies have investigated the temporal dynamics of this network based on direct physiological recordings. In this study, Yan Cui and Dezhong Yao addressed this issue by characterizing the dynamics of local field potentials (LFPs) from the rat DMN during wakefulness and sleep with an exploratory analysis. They constructed a novel coactive micropattern (CAMP) algorithm to evaluate the configurations of rat DMN dynamics and further revealed the relationship between DMN dynamics with different wakefulness and alertness levels. From the gamma activity (40–80 Hz) in the DMN across wakefulness and sleep, three spatially stable CAMPs were detected: a common low-activity level micropattern (cDMN), an anterior high-activity level micropattern (aDMN), and a posterior high-activity level micropattern (pDMN). A dynamic balance across CAMPs emerged during wakefulness and was disrupted in sleep stages. In the slow-wave sleep (SWS) stage, cDMN became the primary activity pattern, whereas aDMN and pDMN were the major activity patterns in the rapid eye movement sleep (REM) stage. In addition, further investigation revealed phasic relationships between CAMPs and the up–down states of the slow DMN activity in the SWS stage. Their study revealed that the dynamic configurations of CAMPs were highly associated with different stages of wakefulness and provided a potential three-state model to describe the DMN dynamics for wakefulness and alertness.

Long-Range Auditory FC in Hearing Loss and Rehabilitation (https://doi.org/10.1089/brain.2020.0814)

Patients with age-related sensorineural hearing loss (HL) may benefit from auditory input amplification by using hearing aids (HAs). However, the impact of both HL and HA-based rehabilitation on central auditory FC is not clear. In this study, Sara Ponticorvo and Fabrizio Esposito, along with their colleagues, evaluated subjects with HL, subjects with normal hearing (NH) in a 3T MRI study. HL patients were analyzed cross-sectionally at baseline (vs. NH subjects), and longitudinally at 6-month follow-up.

Between the two scans, patients used HA 9.5 ± 3.8 h a day. Arterial spin labeling and blood oxygen level-dependent rsfMRI were performed to measure regional perfusion in primary auditory cortex and, from there, to the whole brain. Before each scan, HL patients underwent audiological and neurological assessments. At baseline, HL condition was associated with regional hypoperfusion in right Heschl's gyrus (seed) and negative seed-based FC (anticorrelation) in posterior brain regions. Long-range FC in precuneus correlated negatively with pure tone and speech recognition average thresholds. At 6-month follow-up, HA usage was associated with seed-based FC increase in right superior frontal gyrus and seed-based FC reduction in right middle temporal gyrus. Long-range FC changes in superior frontal gyrus correlated positively with executive function improvements.

Substance P Immunoreactive Fiber Varicosities Appear to Innervate Galaninergic Perikarya in the Human Hypothalamus (https://doi.org/10.1089/brain.2020.0909)

Substance P (SP) is a member of the tachykinin family. In the central nervous system, SP participates, among others, in the regulation of pain, learning, memory, emotion, and sexual functions. In the periphery, SP affects the gastrointestinal, cardiovascular, and urinary systems.

Galanin, like SP, appears to be involved in wide range of physiological functions including cognition, waking and sleep, feeding, mood, blood pressure, reproduction, and development where it acts as a trophic factor. A similar distribution of SP-immunoreactive (SP-IR) fibers and galanin-IR perikarya in the human hypothalamus suggests functional interaction between these neuropeptides. Bertalan Dudas and Istvan Merchenthaler have utilized double-label immunohistochemistry to reveal these putative juxtapositions and found that the majority of galanin-IR neurons receive contacting SP-IR fibers that often cover a significant area of the galaninergic perikarya, forming multiple en passant type contacts. These SP–galanin juxtapositions are located mainly in the basal part of the infundibulum/median eminence, populating the basal periventricular region as well as in the basal perifornical area. The density and the morphology of these associations suggest that these contacts are functional synapses and, therefore, may represent the morphological substrate of the control of SP on multiple functions regulated/modulated by galanin. SP, through galanin, may modulate anterior pituitary hormone secretion as, contrary to SP, high density of galanin immune reactivity is present in the median eminence and, by innervating galanin-IR neurons projecting to other parts of the brain, SP can indirectly modulate their activities.

Finally, I thank all the researchers around the world working during these difficult times to advance research and improve our lives in every corner of the world. With COVID-19 vaccine around the corner, we wish you all a brighter future ahead.