Managing Psychological Challenges in the Subacute ("Afterglow") Window of Psychedelic Drug Effects.

Foot sole somatosensory impairment associated with peripheral neuropathy (PN) is prevalent and a strong independent risk factor for gait disturbance and falls in older adults. Walkasins, a lower-limb sensory prosthesis, has been shown to improve gait and mobility in people with PN by providing afferent input related to foot sole pressure distributions via lower-leg mechanical tactile stimulation. Given that gait and mobility are regulated by sensorimotor and cognitive brain networks, it is plausible improvements in gait and mobility from wearing the Walkasins may be associated with elicited neuroplastic changes in the brain. As such, this study aimed to examine changes in brain network connectivity after 26 weeks of daily use of the prosthesis among individuals with diagnosed PN and balance problems. In this exploratory investigation, assessments of participant characteristics, Functional Gait Assessment (FGA), and resting-state functional magnetic resonance imaging were completed at study baseline and 26 weeks follow-up. We found that among those who have completed the study (N = 8; mean age 73.7 years) we observed a five-point improvement in FGA performance as well as significant changes in network connectivity over the 26 weeks that were correlated with improved FGA performance. Specifically, greater improvement in FGA score over 26 weeks was associated with increased connectivity within the Default Mode Network (DMN; p < 0.01), the Somatosensory Network (SMN; p < 0.01), and the Frontoparietal Network (FPN; p < 0.01). FGA improvement was also correlated with increased connectivity between the DMN and the FPN (p < 0.01), and decreased connectivity between the SMN and both the FPN (p < 0.01) and cerebellum (p < 0.01). These findings suggest that 26 weeks of daily use of the Walkasins device may provide beneficial neural modulatory changes in brain network connectivity via the sensory replacement stimulation that are relevant to gait improvements among older adults with PN.

Foot sole somatosensory impairment associated with peripheral neuropathy (PN) is prevalent and a strong independent risk factor for gait disturbance and falls in older adults. A lower-limb sensory prosthesis providing afferent input related to foot sole pressure distributions via lower-leg vibrotactile stimulation has been demonstrated to improve gait in people with PN. The effects of this device on brain function related to motor control, however, remains equivocal. This study aimed to explore changes in brain network connectivity after six months of daily use of the prosthesis among individuals with diagnosed PN and balance problems. Functional Gait Assessment (FGA) and resting-state functional magnetic resonance imaging were completed before and after the intervention. Preliminary analysis on participants who have completed the study to date (N=5; mean age 76 years) indicated altered connectivity of the sensorimotor network (SMN), frontoparietal network (FPN), and the default mode network (DMN) post-intervention (Z>3.11, unadjusted p<0.05). Participants displayed an average improvement of 5.5 point in the FGA (Minimal Clinically Important Differences>4 for community-dwelling older adults) that was correlated with connectivity changes (unadjusted p<0.05). Specifically, improved FGA was associated with: 1) increased connectivity between the SMN, cerebellum, and occipital cortex; 2) increased connectivity between the FPN, cerebellum, calcarine and intracalcarine; and 3) decreased connectivity between DMN and intracalcarine. These early findings suggest that long-term use of a lower-limb sensory prosthesis may induce neuroplastic changes in brain network connectivity reflecting enhanced bottom-up sensory-attentional processing and suppression of the DMN that are relevant to gait improvements among older adults with PN.

Foot sole somatosensory impairment associated with peripheral neuropathy (PN) is prevalent and a strong independent risk factor for gait disturbance and falls in older adults. A lower-limb sensory prosthesis providing afferent input related to foot sole pressure distributions via lower-leg vibrotactile stimulation has been demonstrated to improve gait in people with PN. The effects of this device on brain function related to motor control, however, remains equivocal. This study aimed to explore changes in brain network connectivity after six months of daily use of the prosthesis among individuals with diagnosed PN and balance problems. Functional Gait Assessment (FGA) and resting-state functional magnetic resonance imaging were completed before and after the intervention. Preliminary analysis on participants who have completed the study to date (N=5; mean age 76 years) indicated altered connectivity of the sensorimotor network (SMN), frontoparietal network (FPN), and the default mode network (DMN) post-intervention (Z>3.11, unadjusted p<0.05). Participants displayed an average improvement of 5.5 point in the FGA (Minimal Clinically Important Differences>4 for community-dwelling older adults) that was correlated with connectivity changes (unadjusted p<0.05). Specifically, improved FGA was associated with: 1) increased connectivity between the SMN, cerebellum, and occipital cortex; 2) increased connectivity between the FPN, cerebellum, calcarine and intracalcarine; and 3) decreased connectivity between DMN and intracalcarine. These early findings suggest that long-term use of a lower-limb sensory prosthesis may induce neuroplastic changes in brain network connectivity reflecting enhanced bottom-up sensory-attentional processing and suppression of the DMN that are relevant to gait improvements among older adults with PN.

Characterizing brain network alterations in cervical spondylotic myelopathy using static and dynamic functional network connectivity and machine learning.

Disclosure: J. Andreano: None. A. Lopez: None. M. Misra: None. Background: Previous research indicates that acute gonadal treatments and fluctuations in ovarian hormone levels over the course of the menstrual cycle can alter patterns of communication between brain regions, as well as producing significant changes in self-reported affect. Recent research from our laboratory indicates changes in both brain connectivity and affect with gender affirming hormone therapy (GAHT). However, the relationship between these changes with GAHT are not well understood. Additionally, it is difficult to separate influences of GAHT and transgender identity in understanding differences in connectvity and affect from cisgender controls. A better understanding of the influence of GAHT on intrinsic network connectivity could point the way towards improved GAHT treatment regimens that minimize affective side effects. Objective: To assess relationships between changes in brain network connectivity and affective changes following GAHT, identifying regions in which changes of connectivity are associated with changes in self-reported affect, and to examine differences in brain network connectivity between transgender participants and cisgender controls. Methods: 14 transgender individuals receiving GAHT (estradiol n =7, testosterone n = 7)) were scanned using functional magnetic imaging (fMRI) at rest at two timepoints: baseline, prior to treatment, and 6 months into GAHT. Additionally, 20 cisgender controls (10 male, 10 female) were scanned once each. The time course of neural activity was computed for each voxel in the brain, then tested for correlation with each other voxel to develop a whole-brain connectome for each participant. Maps of average connectivity to two seeds known to associate with two major networks in the brain (posterior cingulate cortex for Default Mode Network, and anterior insula for Salience Network) were then generated for each participant. Within transgender participants, these maps were then compared between the baseline and 6 month timepoints. Additionally, baseline scans were also compared to scans from cisgender controls corresponding to biological sex. Results: Within-subjects analyses of transgender participants showed diverse associations between changes in seed-based connectivity and changes in self-reported anxiety and depression following GAHT in both the default mode and salience networks. Between subjects comparisons showed significantly higher connectivity within the salience network in trans men relative to cis women, as well as greater inter-network connectivity in this group. In contrast, cis men showed greater connectivity within both default mode and salience networks relative to trans women. Conclusions: Changes in affect following GAHT appear related to GAHT-induced changes in network connectivity; these influences appear independent of differences between cis and transgender individuals. Presentation: 6/2/2024

Disclosure: J. Andreano: None. A. Lopez: None. M. Misra: None. Background: Previous research indicates that acute gonadal treatments and fluctuations in ovarian hormone levels over the course of the menstrual cycle can alter patterns of communication between brain regions, as well as producing significant changes in self-reported affect. Recent research from our laboratory indicates changes in both brain connectivity and affect with gender affirming hormone therapy (GAHT). However, the relationship between these changes with GAHT are not well understood. Additionally, it is difficult to separate influences of GAHT and transgender identity in understanding differences in connectvity and affect from cisgender controls. A better understanding of the influence of GAHT on intrinsic network connectivity could point the way towards improved GAHT treatment regimens that minimize affective side effects. Objective: To assess relationships between changes in brain network connectivity and affective changes following GAHT, identifying regions in which changes of connectivity are associated with changes in self-reported affect, and to examine differences in brain network connectivity between transgender participants and cisgender controls. Methods: 14 transgender individuals receiving GAHT (estradiol n =7, testosterone n = 7)) were scanned using functional magnetic imaging (fMRI) at rest at two timepoints: baseline, prior to treatment, and 6 months into GAHT. Additionally, 20 cisgender controls (10 male, 10 female) were scanned once each. The time course of neural activity was computed for each voxel in the brain, then tested for correlation with each other voxel to develop a whole-brain connectome for each participant. Maps of average connectivity to two seeds known to associate with two major networks in the brain (posterior cingulate cortex for Default Mode Network, and anterior insula for Salience Network) were then generated for each participant. Within transgender participants, these maps were then compared between the baseline and 6 month timepoints. Additionally, baseline scans were also compared to scans from cisgender controls corresponding to biological sex. Results: Within-subjects analyses of transgender participants showed diverse associations between changes in seed-based connectivity and changes in self-reported anxiety and depression following GAHT in both the default mode and salience networks. Between subjects comparisons showed significantly higher connectivity within the salience network in trans men relative to cis women, as well as greater inter-network connectivity in this group. In contrast, cis men showed greater connectivity within both default mode and salience networks relative to trans women. Conclusions: Changes in affect following GAHT appear related to GAHT-induced changes in network connectivity; these influences appear independent of differences between cis and transgender individuals. Presentation: 6/2/2024

.SignificanceNeonatal brain development plays a crucial role in long-term neurodevelopmental outcomes, particularly in preterm infants.AimWe utilized functional near-infrared spectroscopy (fNIRS) to examine the evolution of brain network connectivity in late preterm and term neonates.ApproachNeonates with a gestational age (GA) between 33 and 41 weeks were included in the study. fNIRS headcaps were placed on the neonates after reaching a stable sleep state. fNIRS data were recorded in continuous-wave mode. Multivariate pattern analysis (MVPA) was conducted to identify distributed patterns of connectivity changes.ResultsSignificant developmental changes in brain network connectivity were observed at around 37 weeks of GA, marked by enhanced functional connectivity, particularly within brain network connectivity centered on the parietal lobe (PL). MVPA demonstrated high classification accuracy in distinguishing neonates born before 37 weeks from those born at or after 37 weeks, based on the strength of PL-centered brain connectivity. The accuracy values were as follows: PL = 74.17%, PL-FL = 81.10%, PL-TL = 74.68%, and PL-OL = 67.18%.ConclusionsThese results underscore the critical role of GA in shaping neonatal brain network functional organization and provide valuable insights for early intervention strategies in preterm infants.

This study presents a post hoc analysis of our parent study "Evaluating the Safety and Efficacy of Transcranial Pulse Stimulation on Autism Spectrum Disorder" study which was a double-blind, sham-controlled, randomized controlled trial. In this study, we examined associations between changes in brain network connectivity and cognitive performance in young adolescents (12-17 years) with autism spectrum disorder (ASD) following the administration of transcranial pulse stimulation (TPS) which is considered non-invasive, evidenced-based brain stimulation for neurodegenerative disorders and neuropsychiatric disorders. Our findings indicate that increased connectivity in specific brain networks is associated with improvements in cognitive measures, suggesting that connectivity changes may underpin cognitive changes observed after six TPS intervention. These results highlight potential neural mechanisms underlying cognitive improvements in ASD, although causality cannot be inferred from these associations. Trial Registration: ClinicalTrials.gov identifier: NCT05408793.

BackgroundAlzheimer’s disease (AD) and frontotemporal dementia (FTD) are the two most common neurodegenerative dementias, presenting with similar clinical features that challenge accurate diagnosis. Despite extensive research, the underlying pathophysiological mechanisms remain unclear, and effective treatments are limited. This study aims to investigate the alterations in brain network connectivity associated with AD and FTD to enhance our understanding of their pathophysiology and establish a scientific foundation for their diagnosis and treatment.MethodsWe analyzed preprocessed electroencephalogram (EEG) data from the OpenNeuro public dataset, comprising 36 patients with AD, 23 patients with FTD, and 29 healthy controls (HC). Participants were in a resting state with eyes closed. We estimated the average functional connectivity using the Phase Lag Index (PLI) for lower frequencies (delta and theta) and the Amplitude Envelope Correlation with leakage correction (AEC-c) for higher frequencies (alpha, beta, and gamma). Graph theory was applied to calculate topological parameters, including mean node degree, clustering coefficient, characteristic path length, global and local efficiency. A permutation test was then utilized to assess changes in brain network connectivity in AD and FTD based on these parameters.ResultsBoth AD and FTD patients showed increased mean PLI values in the theta frequency band, along with increases in average node degree, clustering coefficient, global efficiency, and local efficiency. Conversely, mean AEC-c values in the alpha frequency band were notably diminished, which was accompanied by decreases average node degree, clustering coefficient, global efficiency, and local efficiency. Furthermore, AD patients in the occipital region showed an increase in theta band node degree and decreased alpha band clustering coefficient and local efficiency, a pattern not observed in FTD.ConclusionsOur findings reveal distinct abnormalities in the functional network topology and connectivity in AD and FTD, which may contribute to a better understanding of the pathophysiological mechanisms of these diseases. Specifically, patients with AD demonstrated a more widespread change in functional connectivity, while those with FTD retained connectivity in the occipital lobe. These observations could provide valuable insights for developing electrophysiological markers to differentiate between the two diseases.

Asymptomatic carotid artery stenosis (CAS) and occlusion (CAO) disrupt cerebral hemodynamics. There are few studies on the brain network changes and compensation associated with the progression from chronic CAS to CAO. In the current study, our goal is to improve the understanding of the specific abnormalities and compensatory phenomena associated with the functional connection in patients with CAS and CAO. In this prospective study, 27 patients with CAO, 29 patients with CAS, and 15 healthy controls matched for age, sex, education, handedness, and risk factors underwent neuropsychological testing and resting-state functional magnetic resonance (rs-fMRI) imaging simultaneously; graph theoretical analysis of brain networks was performed to determine the relationship between changes in brain network connectivity and the progression from internal CAS to CAO. The global properties of the brain network assortativity (p = 0.002), hierarchy (p = 0.002), network efficiency (p = 0.011), and small-worldness (p = 0.009) were significantly more abnormal in the CAS group than in the control and CAO groups. In patients with CAS and CAO, the nodal efficiency of key nodes in multiple brain regions decreased, while the affected hemisphere lost many key functional connections. In this study, we found that patients with CAS showed grade reconstruction, invalid connections, and other phenomena that impaired the efficiency of information transmission in the brain network. A compensatory functional connection in the contralateral cerebral hemisphere of patients with CAS and CAO may be an important mechanism that maintains clinical asymptomatic performance. This study not only reveals the compensation mechanism of cerebral hemisphere ischemia but also validates previous explanations for brain function connectivity, which can help provide interventions in advance and reduce the impairment of higher brain functions. This trial is registered with Clinical Trial Registration-URL http://www.chictr.org.cn and Unique identifier ChiCTR1900023610.

The topological attributes of structural covariance networks (SCNs) based on fractal dimension (FD) and changes in brain network connectivity were investigated using graph theory and network-based statistics (NBS) in patients with noise-induced hearing loss (NIHL). High-resolution 3D T1 images of 40 patients with NIHL and 38 healthy controls (HCs) were analyzed. FD-based Pearson correlation coefficients were calculated and converted to Fisher’s Z to construct the SCNs. Topological attributes and network hubs were calculated using the graph theory. Topological measures between groups were compared using nonparametric permutation tests. Abnormal connection networks were identified using NBS analysis. The NIHL group showed a significantly increased normalized clustering coefficient, normalized characteristic path length, and decreased nodal efficiency of the right medial orbitofrontal gyrus. Additionally, the network hubs based on betweenness centrality and degree centrality were both the right transverse temporal gyrus and left parahippocampal gyrus in the NIHL group. The NBS analysis revealed two subnetworks with abnormal connections. The subnetwork with enhanced connections was mainly distributed in the default mode, frontoparietal, dorsal attention, and somatomotor networks, whereas the subnetwork with reduced connections was mainly distributed in the limbic, visual, default mode, and auditory networks. These findings demonstrate the abnormal topological structure of FD-based SCNs in patients with NIHL, which may contribute to understand the complex mechanisms of brain damage at the network level, providing a new theoretical basis for neuropathological mechanisms.

Current medications have not been effective in reducing the prevalence of mental illness worldwide. The prevalence of illnesses such as treatment-resistant depression has increased despite the widespread use of a broad set of psychopharmaceuticals. Transcranial magnetic stimulation and ketamine therapy are making great strides in improving treatment-resistant depression outcomes but they have limitations. New psychotherapeutics are required that specifically target the underlying cellular pathologies leading to neuronal atrophy. This neuronal atrophy model is supplanting the long-held neurotransmitter deficit hypothesis to explain mental illness. Interest in psychedelics as therapeutic molecules to treat mental illness is experiencing a 21st-century reawakening that is on the cusp of a transformation. Psilocybin is a pro-drug, found in various naturally occurring mushrooms, that is dephosphorylated to produce psilocin, a classic tryptamine psychedelic functional as a 5-hydroxytryptamine 2A receptor agonist. We have focused this review to include studies in the last two years that suggest psilocybin promotes neuronal plasticity, which may lead to changes in brain network connectivity. Recent advancements in clinical trials using pure psilocybin in therapy suggest that it may effectively relieve the symptoms of depression in patients diagnosed with major depressive disorder and treatment-resistant depression. Sophisticated cellular and molecular experiments at the systems level have produced evidence that demonstrates psilocybin promotes neuritogenesis in the mouse brain - a mechanism that may address the root cause of depression at the cellular level. Finally, studies with psilocybin therapy for major depressive disorder suggest that this ancient molecule can promote functionally connected intrinsic networks in the human brain, resulting in durable improvements in the severity of depressive symptoms. Although further research is necessary, the prospect of using psilocybin for the treatment of mental illness is an enticing possibility.

Atopic dermatitis (AD) is a T helper 2-mediated inflammatory condition, resulting in erythematous and pruritic skin. Patients with AD often have chronic itching, leading to subsequent scratching, which further damages the skin and exacerbates cutaneous inflammation. Patients with AD often experience severe itching–scratching behaviour at night, leading to poor sleep quality. Chronic sleep disturbances during early childhood may alter brain development and neural connectivity, contributing to the increased risk of neuropsychological problems in patients with AD. In Mind &amp; Skin, we aim to understand how inflammation and itch experienced by patients with AD can affect sleep, functional connectivity and cognitive function. Adolescent patients with AD were recruited from paediatric allergy and dermatology clinics, whereas healthy controls were recruited from patients’ healthy siblings or through internal newsletters. Resting-state functional magnetic resonance imaging (fMRI) scans were acquired from the participants. The participants also completed a series of cognitive tasks using the Maudsley Attention and Response Suppression task battery to assess their attention, motor and cognitive inhibition, and time perception. Eighteen adolescent patients with AD with a range of disease severities (median age 15 years, range 12–18) and nine healthy controls (median age 15 years, range 12–16) were recruited for the study. The patients with AD had a median Eczema Area and Severity Index score of 4.45 (range 0.8–30.9). Sixteen resting-state networks were identified from fMRI analysis. The patients with AD, relative to healthy controls, had significant increases in functional connectivity within the somatosensory motor network, likely associated with chronic itching–scratching behaviours. The patients with AD exhibited reduced functional connectivity between the visual cortex and the temporoparietal network compared with healthy controls. The cohort with AD also showed decreased performance in the Simon (cognitive interference inhibition) and time discrimination tasks compared with healthy controls. The temporoparietal junction (TPJ) has been suggested to be a multisensory integration hub and is crucial for visual–spatial attention to external stimuli. Enhanced somatosensory connectivity in AD may disrupt TPJ–visual connectivity, potentially affecting selective visual–spatial attention and contributing to reduced ability in the Simon task. Our preliminary findings provide insights into the neurocognitive effects of the chronic itch–scratch behaviours associated with AD and how they may be associated with changes in brain network connectivity. These insights highlight the need for a multidisciplinary approach to address the dermatological and neurodevelopmental impacts of AD, with future studies aimed at identifying targeted interventions that mitigate these specific cognitive difficulties and improve quality of life.

It has been shown in the literature that Autism Spectrum Disorder (ASD) is associated with changes in brain network connectivity. Therefore, we investigate, if it is possible to capture any significant difference between brain connections of healthy subjects and ASD patients using resting-state fMRI time-series. To this end, we have developed large-scale Extended Granger Causality (lsXGC), which combines dimension reduction with source time-series augmentation and uses predictive time-series modeling for estimating directed causal relationships among resting-state fMRI time-series. This method is a multivariate approach, since it is capable of identifying the influence of each time-series on any other time-series in the presence of all other time-series of the underlying dynamic system. Here, we investigate whether this model can serve as a biomarker for classifying ASD patients from typical controls using a subset of 59 subjects of the Autism Brain Imaging Data Exchange II (ABIDE II) data repository. In this study, we use brain connections as features for classification and estimate them by lsXGC. As a reference method, we compare our results with cross-correlation, which is typically used in the literature as a standard measure of functional connectivity. After feature extraction, we perform feature selection by Kendall’s Tau rank correlation coefficient followed by classification using a Support Vector Machine (SVM). In order to evaluate the diagnostic accuracy of lsXGC, we compare its classification performance with cross-correlation. Within a cross-validation scheme of 100 different training/test data splits, we obtain a mean accuracy range of [0.7,0.81] and a mean Area Under the Receiver Operator Characteristic Curve (AUC) range of [0.78,0.85] across all tested numbers of features for lsXGC, which is significantly better than results obtained with cross-correlation namely mean accuracy of [0.57,0.61] and mean AUC of [0.54,0.59], which clearly demonstrates the applicability of lsXGC as a potential biomarker for ASD.

It has been shown in the literature that marijuana use is associated with changes in brain network connectivity. We investigate whether large-scale Extended Granger Causality (lsXGC) can capture such changes using restingstate fMRI. This method combines dimension reduction with source time-series augmentation and uses predictive time-series modeling for estimating directed causal relationships among fMRI time-series. It is a multivariate approach, since it is capable of identifying the interdependence of time-series in the presence of all other timeseries of the underlying dynamic system. Here, we investigate whether this model can serve as a biomarker for classifying marijuana users from typical controls using 126 adult subjects with a childhood diagnosis of ADHD from the Addiction Connectome Preprocessed Initiative (ACPI) database. We use brain connections estimated by lsXGC as features for classification. After feature extraction, we perform feature selection by Kendall’s-tau rank correlation coefficient followed by classification using a support vector machine. As a reference method, we compare our results with cross-correlation, which is typically used in the literature as a standard measure of functional connectivity. Within a cross-validation scheme of 100 different training/test (90%/10%) data splits, we obtain a mean accuracy range of [0.714, 0.985] and a mean Area Under the receiver operating characteristic Curve (AUC) range of [0.779, 0.999] across all tested numbers of features for lsXGC, which is significantly better than results obtained with cross-correlation, namely mean accuracy of [0.728, 0.912] and mean AUC of [0.825, 0.969]. Our results suggest the applicability of lsXGC as a potential biomarker for marijuana use.