bioRxiv Subject Collection: Neuroscience's Journal

Movement quality moderates the effect of spatially congruent cues on the stability of rhythmic bimanual finger movements.
Spatially congruent cues increase the speed of bimanual reach decisions compared to abstract symbolic cues, particularly for asymmetric reaches. Asymmetric rhythmic bimanual movements are less stable than symmetric rhythmic movements, but it is not well understood if spatially congruent cues similarly increase the stability of asymmetric rhythmic bimanual movements. To address this question, in Experiment 1, participants performed symmetric and asymmetric bimanual rhythmic finger tapping movements at different movement frequencies in time with flickering spatially congruent and abstract symbolic stimuli. As expected, symmetric movements were more stable. Spatially congruent cues similarly increased the stability of symmetric and asymmetric movements compared to abstract symbolic cues. The benefits of spatial congruence and movement symmetry were restricted to high movement frequencies (>2 hertz). To better understand if the emergence of these effects at high movement frequencies was driven by a change in movement strategy, in Experiment 2, video of the hands was concurrently recorded during task performance. Markerless motion tracking software revealed that participants switched from discontinuous to continuous movement strategies with increasing movement frequency. Since discontinuous and continuous movements are thought to be controlled by distinct neuro-cognitive systems, this might explain why the beneficial effects of spatial congruence and response symmetry emerged only at high movement frequencies. Overall, results from the current study indicate that the perceptual quality of the stimulus use to cue movement frequency can have powerful effects on the stability of rhythmic bimanual movements, but that these effects may depend on whether discontinuous or continuous movement strategies are selected.

Evaluating a Novel High-Density EEG Sensor Net Structure for Improving Inclusivity in Infants with Curly or Tightly Coiled Hair
Electroencephalography (EEG) is an important tool in the field of developmental cognitive neuroscience for indexing neural activity. However, racial biases persist in EEG research that limit the utility of this tool. One bias comes from the structure of EEG nets/caps that do not facilitate equitable data collection across hair textures and types. Recent efforts have improved EEG net/cap design, but these solutions can be time-intensive, reduce sensor density, and are more difficult to implement in younger populations. The present study focused on testing EEG sensor net designs over infancy. Specifically, we compared EEG data quality and retention between two high-density saline-based EEG sensor net designs from the same company (Magstim EGI, Whitland, UK) within the same infants during a baseline EEG paradigm. We found that within infants, the tall sensor nets resulted in lower impedances during collection, including lower impedances in the key online reference electrode for those with greater hair heights and resulted in a greater number of usable EEG channels and data segments retained during pre-processing. These results suggest that along with other best practices, the modified tall sensor net design is useful for improving data quality and retention in infant participants with curly or tightly-coiled hair.

How to measure functional connectivity using resting-state fMRI? A comprehensive empirical exploration of different connectivity metrics
Background: Functional connectivity in the context of functional magnetic resonance imaging is typically quantified by Pearson or partial correlation between regional time series of the blood oxygenation level dependent signal. However, a recent interdisciplinary methodological work proposes more than 230 different metrics to measure similarity between different types of time series. Objective: Hence, we systematically evaluated how the results of typical research approaches in functional neuroimaging vary depending on the functional connectivity metric of choice. We further explored which metrics most accurately detect neural decline induced by age and malignant brain tumors, aiming to initiate a debate on how best assessing brain connectivity in functional neuroimaging research. Methods: We addressed both research questions using four independent neuroimaging datasets, comprising multimodal data from a total of 1187 individuals. We analyzed resting-state functional sequences to calculate functional connectivity using 20 representative metrics from four distinct mathematical domains. We further used T1- and T2-weighted images to compute regional brain volumes, diffusion-weighted imaging data to build structural connectomes, and pseudo-continuous arterial spin labeling to measure regional brain perfusion. Results: First, our results demonstrate that the results of typical functional neuroimaging approaches differ fundamentally depending on the functional connectivity metric of choice. Second, we show that correlational and distance metrics are most appropriate to cover neural decline induced by age. In this context, partial correlation performs worse than other correlational metrics. Third, our findings suggest that the FC metric of choice depends on the utilized scanning parameters, the regions of interest, and the individual investigated. Lastly, beyond the major objective of this study, we provide evidence in favor of brain perfusion measured via pseudo-continuous arterial spin labeling as a robust neural entity mirroring age-related neural and cognitive decline. Conclusion: Our empirical evaluation supports a recent theoretical functional connectivity framework. Future functional imaging studies need to comprehensively define the study-specific theoretical property of interest, the methodological property to assess the theoretical property, and the confounding property that may bias the conclusions.

Characterising the stimulus-response function of mouse C-low threshold mechanoreceptors to mechanical stimuli in vivo
C-low threshold mechanoreceptors (C-LTMRs) in animals (termed C-tactile (CT) fibres in humans) are a subgroup of C-fibre primary afferents, which innervate hairy skin and respond to low threshold punctate indentations and brush stimuli. These afferents respond to gentle, touch stimuli and are implicated in mediating pleasant/affective touch. These afferents have traditionally been studied using low-throughput, technically challenging approaches, including microneurography in humans and teased fibre electrophysiology in other mammals. Here we suggest a new approach to studying genetically labelled C-LTMRs using in vivo calcium imaging. We used an automated rotating brush stimulus and Von Frey filaments, applied to the hairy skin of anaesthetised mice to mirror light and affective touch. Simultaneously we visualised changes in C-LTMR activity and confirmed that these neurons are sensitive to low-threshold punctate mechanical stimuli and brush stimuli with a strong preference for slow brushing speeds. We also reveal that C-LMTRs are directionally sensitive, showing more activity when brushed against the natural orientation of the hair. We present in vivo calcium imaging of genetically labelled C-LTMRs as a useful approach that can reveal new aspects of C-LTMR physiology.

CACNA1A haploinsufficiency leads to reduced synaptic function and increased intrinsic excitability
Haploinsufficiency of the CACNA1A gene, encoding the pore-forming 1 subunit of P/Q-type voltage-gated calcium channels, is associated with a clinically variable phenotype ranging from cerebellar ataxia, to neurodevelopmental syndromes with epilepsy and intellectual disability. To understand the pathological mechanisms of CACNA1A loss-of-function variants, we characterized a human neuronal model for CACNA1A haploinsufficiency, by differentiating isogenic induced pluripotent stem cell lines into glutamatergic neurons, and investigated the effect of CACNA1A haploinsufficiency on mature neuronal networks through a combination of electrophysiology, gene expression analysis, and in silico modeling. We observed an altered network synchronization in CACNA1A{+/-} networks alongside synaptic deficits, notably marked by an augmented contribution of GluA2-lacking AMPA receptors. Intriguingly, these synaptic perturbations coexisted with increased non-synaptically driven activity, as characterized by inhibition of NMDA and AMPA receptors on micro-electrode arrays. Single-cell electrophysiology and gene expression analysis corroborated this increased intrinsic excitability through reduced potassium channel function and expression. Moreover, we observed partial mitigation of the CACNA1A{+/-} network phenotype by 4-aminopyridine, a therapeutic intervention for episodic ataxia type 2. In summary, our study pioneers the characterization of a human induced pluripotent stem cell-derived neuronal model for CACNA1A haploinsufficiency, and has unveiled novel mechanistic insights. Beyond showcasing synaptic deficits, this neuronal model exhibited increased intrinsic excitability mediated by diminished potassium channel function, underscoring its potential as a therapeutic discovery platform with predictive validity.

Dissociable dorsal medial prefrontal cortex ensembles are necessary for cocaine seeking and fear conditioning in mice
The dmPFC plays a dual role in modulating drug seeking and fear-related behaviors. Learned associations between cues and drug seeking are encoded by a specific ensemble of neurons. This study explored the stability of a dmPFC cocaine seeking ensemble over two weeks and its influence on persistent cocaine seeking and fear memory retrieval. In the first series of experiments, we trained TetTag mice in cocaine self-administration and tagged strongly activated neurons with EGFP during the initial day 7 cocaine seeking session. Subsequently, a follow-up seeking test was conducted two weeks later to examine ensemble reactivation between two seeking sessions via c-Fos immunostaining. In the second series of experiments, we co-injected viruses expressing TRE-cre and a cre-dependent inhibitory PSAM-GlyR into the dmPFC of male and female c-fos-tTA mice to enable tagging of cocaine seeking ensemble or cued fear ensemble neurons with an inhibitory chemogenetic receptors. Then we investigated their contribution to subsequent cocaine seeking and fear recall during inhibition of the tagged ensemble by administering uPSEM792s (0.3 mg/kg), a selective ligand for PSAM-GlyR. In both sexes, there was a positive association between the persistence of cocaine seeking and the proportion of reactivated EGFP+ neurons within the dmPFC. More importantly, inhibition of the cocaine seeking ensemble suppressed cocaine seeking, but not recall of fear memory, while inhibition of the fear ensemble reduced conditioned freezing but not cocaine seeking. The results demonstrate that cocaine and fear recall ensembles in the dmPFC are stable but largely exclusive from one another.

VLK drives extracellular phosphorylation of EphB2 to govern the EphB2-NMDAR interaction and injury-induced pain
Phosphorylation of hundreds of protein extracellular domains is mediated by two kinase families, yet the significance of these kinases is underexplored. Here, we find that the presynaptic release of the tyrosine directed-ectokinase, Vertebrate Lonesome Kinase (VLK/Pkdcc), is necessary and sufficient for the direct extracellular interaction between EphB2 and GluN1 at synapses, for phosphorylation of the ectodomain of EphB2, and for injury-induced pain. Pkdcc is an essential gene in the nervous system, and VLK is found in synaptic vesicles, and is released from neurons in a SNARE-dependent fashion. VLK is expressed by nociceptive sensory neurons where presynaptic sensory neuron-specific knockout renders mice impervious to post-surgical pain, without changing proprioception. VLK defines an extracellular mechanism that regulates protein-protein interaction and non-opioid-dependent pain in response to injury.

Classification and analysis of retinal interneurons by computational structure under natural scenes
Inhibitory neurons are diverse across the brain, but in the visual system we lack the ability to functionally classify these neurons under complex natural stimuli. Here we take the approach of classifying mouse retinal amacrine cell responses to natural scenes using optical recording and an interpretable neural network model. We fit amacrine cell responses to a two-layer convolutional neural network model of a class shown previously to accurately capture ganglion cell responses to natural scenes. Using an approach from interpretable machine learning, we determined for each stimulus the model interneurons that generated each amacrine response, analogous to the set of bipolar cells that target the amacrine population. From this analysis we clustered amacrine cells not by their natural scene responses, but by the model presynaptic neurons that constructed those responses, conservatively finding approximately seven groups by this approach. By analyzing the set of model presynaptic input neurons for each amacrine cluster, we find that distributed rather than dedicated inputs generate natural scene responses for different amacrine cell types. Additional analyses revealed distinct transient and sustained modes exhibited by the network during the response to simple flashes. These results give insight into the computational structure of how the diverse amacrine cell population responds to natural scenes, and generate multiple quantitative hypotheses for how synaptic inputs generate those responses.

Impaired functional brain-heart interplay sustains emotion dysregulation in depressed individuals
Depression is a leading worldwide cause of mental disorders and disability, strongly affecting emotional processing and regulation. Its dysfunctional psycho-physiological dynamics may be part of the a nervous-system-wise symptomatology, impacting not only patients' psyche but also significantly influencing functional cardiovascular dynamics. Therefore, depression serves as an exemplary pathological manifestation of the dysfunctional interaction between the central and autonomic nervous systems. While recent literature has been developing specific techniques to quantify such interactions, often referred to as functional Brain-Heart Interplay (BHI), the quantitative role of BHI dynamics in depression is largely unknown. This study aims to experimentally unveil BHI patterns specific to emotional regulation and processing in subjects exhibiting depressive symptoms compared to healthy controls. Results were gathered from a cohort of 72 individuals and indicate that depressive symptoms are associated with a continuous efferent central-to-peripheral hyperactivity and an afferent peripheral-to-central hypoactivity. This hypoactivity appears to be specific to negative emotional processing. This study offers novel insights into the systemic investigation of the neuro-physiological bases of depression.

Parent attention-orienting behavior is associated with neural entropy in infancy
Parents play a significant role in directing infant's attention to environmental stimuli via joint attention. We hypothesized that infants whose parents provide more bids for joint attention will display a more complex neural response when viewing social scenes. Sixty-one 8-month-old infants underwent electroencephalography (EEG) while viewing videos of joint- and parallel-play and participated in a parent-infant free play interaction. EEG data was analyzed using multiscale entropy, which quantifies moment-to-moment neural variability. Free play interactions were coded for parent alternating gaze, a behavioral mechanism for directing attention to environmental cues. We found a significant positive association between parent alternating gaze and neural entropy in frontal and central brain regions. These results suggest a relationship between parent behavior and infant neural mechanisms that regulate social attention, underlying the importance of parent cues in the formation of neural networks in infancy.

The Origin of Movement Biases During Reaching
Goal-directed movements can fail due to errors in our perceptual and motor systems. While these errors may arise from random noise within these sources, they also reflect systematic motor biases that vary with the location of the target. The origin of these systematic biases remains controversial. Drawing on data from an extensive array of reaching tasks conducted over the past 30 years, we evaluated the merits of various computational models regarding the origin of motor biases. Contrary to previous theories, we show that motor biases do not arise from systematic errors associated with the sensed hand position during motor planning or from the biomechanical constraints imposed during motor execution. Rather, motor biases are primarily caused by a misalignment between eye-centric and the body-centric representations of position. This model can account for motor biases across a wide range of contexts, encompassing movements with the right versus left hand, proximal and distal effectors, visible and occluded starting positions, as well as before and after sensorimotor adaptation.

Dynamics of Brain Connectivity across the Alzheimer's Disease Spectrum: a magnetoencephalography study
Alzheimer's disease (AD) represents a major challenge in neurodegenerative disease research, characterized by a complex pathophysiology that involves not only structural but also functional changes in the brain. While changes in static functional connectivity have already been linked to AD, there is still a lack of research studying dynamic functional connectivity (dFC) across the AD continuum, which could be crucial for identifying potential biomarkers for early diagnosis and tracking disease progression. This study leverages the high temporal resolution of MEG to dissect the dynamics of brain connectivity alterations across various stages of AD and their association with cognitive decline and structural brain changes.321 participants were included in this study, categorized into healthy controls, subjective cognitive decline (SCD), and mild cognitive impairment (MCI) groups. Amplitude Envelope Correlation with leakage correction was calculated over MEG signals with a sliding window, and the correlation across trials was studied to assess dFC at whole-brain and node level. Finally, we explored dFC associations with cognitive scores, grey matter volume, and white matter fractal anisotropy. The study unveils a significant reduction in whole-brain dFC, especially within the alpha and beta frequency bands, as individuals progress along the AD continuum. Notably, the frontal and temporal lobes and regions within the default mode network exhibited pronounced dFC reductions. Finally, this dFC decline significantly correlated with cognitive performance deterioration and structural brain changes, suggesting the potential of the proposed dFC metric as sensitive indicator for monitoring disease progression. This investigation provides crucial insights into the temporal dynamics of brain connectivity alterations in the early spectrum of AD, underlining the importance of dFC changes as reflective of cognitive and anatomical degeneration. The findings hint towards a strong relationship between connectivity profiles and white matter integrity, especially for high frequency activity in the association cortices.

A brain-to-text framework of decoding natural tonal sentences
Speech brain-computer interfaces (BCIs) directly translate brain activity into speech sound and text, yet decoding tonal languages like Mandarin Chinese poses a significant, unexplored challenge. Despite successful cases in non-tonal languages, the complexities of Mandarin, with its distinct syllabic structures and pivotal lexical information conveyed through tonal nuances, present challenges in BCI decoding. Here we designed a brain-to-text framework to decode Mandarin tonal sentences from invasive neural recordings. Our modular approach dissects speech onset, base syllables, and lexical tones, integrating them with contextual information through Bayesian likelihood and the Viterbi decoder. The results demonstrate accurate tone and syllable decoding under variances in continuous naturalistic speech production, surpassing previous intracranial Mandarin tonal syllable decoders in decoding accuracy. We also verified the robustness of our decoding framework and showed that the model hyperparameters can be generalized across participants of varied gender, age, education backgrounds, pronunciation behaviors, and coverage of electrodes. Our pilot study shed lights on the feasibility of more generalizable brain-to-text decoding of natural tonal sentences from patients with high heterogeneities.

In vivo optogenetics using a Utah Optrode Array with enhanced light output and spatial selectivity
Optogenetics allows manipulation of neural circuits in vivo with high spatial and temporal precision. However, combining this precision with control over a significant portion of the brain is technologically challenging (especially in larger animal models). Here, we have developed, optimised, and tested in vivo, the Utah Optrode Array (UOA), an electrically addressable array of optical needles and interstitial sites illuminated by 181 LEDs and used to optogenetically stimulate the brain. The device is specifically designed for non-human primate studies. Thinning the combined LED and needle backplane of the device from 300 m to 230 m improved the efficiency of light delivery to tissue by 80%, allowing lower LED drive currents, which improved power management and thermal performance. The spatial selectivity of each site was also improved by integrating an optical interposer to reduce stray light emission. These improvements were achieved using an innovative fabrication method to create an anodically bonded glass/silicon substrate with through-silicon vias etched, forming an optical interposer. Optical modelling was used to demonstrate that the tip structure of the device had a major influence on the illumination pattern. The thermal performance was evaluated through a combination of modelling and experiment, in order to ensure that cortical tissue temperatures did not rise by more than 1{degrees}C. The device was tested in vivo in the visual cortex of macaque expressing ChR2-tdTomato in cortical neurons. It was shown that the strongest optogenetic response occurred in the region surrounding the needle tips, and that the extent of the optogenetic response matched the predicted illumination profile based on optical modelling - demonstrating the improved spatial selectivity resulting from the optical interposer approach. Furthermore, different needle illumination sites generated different patterns of low-frequency potential (LFP) activity.

Consequences of neuronal morphology for spatially precise optogenetic stimulation
Optogenetic stimulation has recently proven effective for vision restoration in the human eye, underlining its clinical potential. Its application in sensory cortices could exploit the spatial organization of stimulus feature encoding along the cortical surface to induce artificial perception for restoring a lost sense. However, engaging sensory neuronal populations requires high stimulation precision, which is potentially limited by spatially extending neuronal morphology. Here, we characterize how morphology impacts spatial stimulation precision using an experimentally validated computational model. We show that morphology limits precision at a scale of several hundred micrometers and that the spatial distribution of direct neuronal activation is non-linearly dependent on the stimulation intensity. We compare precision in pyramidal neurons from layers 2/3 and 5 and explore the potential of improving precision through preferentially somatic opsin expression and stimulator design, revealing complex relationships. Our findings have important implications for interpreting existing experimental data and for optimizing future optogenetic interventions.

Single-cell sequencing of rodent ventral pallidum reveals diverse neuronal subtypes with non-canonical interregional continuity
The ventral pallidum (VP) was defined as a basal ganglia nucleus with dense input from ventral striatum. To further investigate a VP regional identity, we conducted a cross-species transcriptional characterization of VP cell types. We performed single nucleus RNA-sequencing of VP tissue from mice and rats and identified 16 VP neuronal subclasses with striking cross-species conservation. VP GABAergic neurons were surprisingly heterogeneous, consisting of 14 sub-classes from 3 developmental classes. Combining our sequencing data with a spatial atlas revealed that all VP subclasses extended beyond the traditional borders of VP. Integrating our VP data with prior sequencing data from striatal, hypothalamic, and extended amygdalar tissue confirmed that cell types are shared among these regions. Due to the role of VP in feeding behavior, we also assessed the transcriptional impact of high-fat diet consumption, which induced altered expression of genes involved in oxidative phosphorylation and inhibitory signaling. Overall, our results demonstrate that VP is not a transcriptionally discrete nucleus; rather, VP contains cell types with diverse expression patterns that overlap with regions beyond the basal ganglia.

Unraveling the Time-Frequency Features of Emotional Regulation: Based on an Interpretable XGBoost-SHAP Analytical Framework
Negative emotions, while crucial for survival, can lead to adverse health effects if not managed properly. Our understanding of temporal EEG changes during emotion regulation is limited. To address this gap, this study employs interpretable machine learning techniques, XGBoost-SHAP model, to analyze EEG data. This study investigates the neural mechanisms underlying emotion regulation, with a focus on EEG oscillations in the lateral prefrontal area channels (F3, F4, F7, F8) across four specific frequency bands (Alpha, Beta, Theta, Delta). By identifying predictive features and patterns, this approach offers insights into the temporal dynamics of emotion regulation and the involvement of specific brain regions, enhancing our understanding of emotional processing and providing avenues for effective interventions. The findings reveal a significant relationship between specific EEG feature changes and emotional ratings during the emotion regulation process. The LPFC emerges as central in cognitive control and emotional regulation. These results highlight the LPFCs rapid and effective role in regulating complex emotional dynamics, crucial for understanding and treating emotional disorders. The study underscores the importance of machine learning in elucidating neural mechanisms and guiding personalized interventions for emotional well-being.

Neurodevelopmentally rooted epicenters in schizophrenia: sensorimotor-association spatial axis of cortical thickness alterations
Pathologic perturbations in schizophrenia have been suggested to propagate via the functional and structural connectome across the lifespan. Yet how the connectome guides early cortical reorganization of developing schizophrenia remains unknown. Here, we used early-onset schizophrenia (EOS) as a neurodevelopmental disease model to investigate putative early pathologic origins that propagate through the functional and structural connectome. We compared 95 patients with antipsychotic-naive first-episode EOS and 99 typically developing controls (7-17 years of age, 120 females). Whereas patients showed widespread cortical thickness reductions, thickness increases were observed in primary cortical areas. Using normative connectomics models, we found that epicenters of thickness reductions were situated in association regions linked to language, affective, and cognitive functions, while epicenters of increased thickness in EOS were located in sensorimotor regions subserving visual, somatosensory, and motor functions. Using post-mortem transcriptomic data of six donors, we observed that the epicenter map differentiated oligodendrocyte-related transcriptional changes at its sensory apex and the association end was related to expression of excitatory/inhibitory neurons. More generally, we observed that the epicenter map was associated with neurodevelopmental disease gene dysregulation and human accelerated region genes, suggesting potential shared genetic determinants across various neurodevelopmental disorders. Taken together, our results underscore the developmentally rooted pathologic origins of schizophrenia and their transcriptomic overlap with other neurodevelopmental diseases.

Nfib regulates progenitor competence in maturation of GABAergic neurons
Inhibitory neurons of the telencephalon are generated from progenitors in the ganglionic eminences that mature and differentiate into specialized cell types. Here, we used single cell transcriptomics and single cell chromatin accessibility together with lineage tracing and birthdating techniques to investigate the influence of progenitor competence on the development of GABAergic precursors. We found that the timing of neurogenesis influences the maturation competence of progenitors to develop towards a fully functional state, but not their differentiation competence to evolve into transcriptomically diverse states. The underlying mechanism defining maturation competence was chromatin priming, orchestrated by the transcription factor Nfib in collaboration with regulators of inhibitory neuron development. Finally, transplantation experiments revealed an interplay between both intrinsic and extrinsic cues acting upon maturation competence. These findings identify a mechanism that coordinates inhibitory neuron development by changing its maturation to achieve maximum adaptability to their environment.

Anxiety-related attentional changes and their relation to freezing of gait in people with Parkinson's: a cross validation of the Adapted Gait Specific Attentional Profile (G-SAPa)
BackgroundAnxiety often exacerbates freezing of gait (FOG) in people with Parkinsons (PwP). Research shows that anxiety-related cognitive processes and associated processing inefficiencies, such as conscious movement processing and ruminations, can substantially impact movement control. However, the impact of these attentional changes on FOG remains largely unexplored. We therefore aimed to (i) validate a questionnaire designed to measure relevant subscales (adapted Gait-Specific Attentional Profile (G-SAPa)) in PwP, and (ii) assess if G-SAPa-subscales (Physiological Arousal, Conscious Movement Processing (CMP), Rumination, and Processing Inefficiencies) are associated with self-reported FOG frequency.

MethodsWe recruited 440 PwP (Mage = 65.5{+/-}8.7; 5.8{+/-}5.0 years since diagnosis) across the UK. Participants completed an adapted 10-item G-SAP (1-5 Likert scale), and questions on demographics, years since diagnosis, self-reported balance problems, other Parkinsons symptoms, and FOG frequency (scale of 0: "never freeze" to 4: "every day"). We assessed G-SAPas internal consistency (alpha), and structural validity (confirmatory factor analysis). Ordinal regression was used to explore associations between G-SAPa subscale scores and FOG frequency.

ResultsThe G-SAPas internal consistency was high (>0.61). Confirmatory factor analysis showed acceptable to good model fit ({chi}2(29)=82.833, p<0.001; {chi}2/df=2.856; CFI=0.976; GFI=0.963; RMSEA=0.066; SRMR=0.035). Measurement invariance testing revealed that the Physiological Arousal and CMP subscale scores were less strongly correlated for PwP with FOG (PwP+FOG, r=.52) compared to PwP without FOG (PwP-FOG, r=.79; p=0.001). Higher Rumination (OR: 1.323, 95% CI: [1.214-1.440]) and Physiological Arousal (OR: 1.195, 95% CI: [1.037-1.377]) were significantly associated with higher FOG frequency, when controlling for age, time since diagnosis and balance/gait problems.

ConclusionsThe G-SAPa is a reliable self-report tool to measure attentional factors implicated in influencing FOG. Rumination scores were most strongly associated with freezing frequency. Such ruminations likely disrupt conscious goal-directed behaviour - an important compensatory process in maintaining motor performance in PwP - and have been associated with perceptions of increased physiological arousal. Indeed, PwP+FOG demonstrated weaker correlation between CMP and Physiological Arousal compared to PwP-FOG, suggesting a relative inability to engage in compensatory goal-directed attentional focus. The G-SAPa represents a short and convenient method for identifying potentially maladaptive anxiety-related attentional processes impacting FOG in research and clinical contexts.

Establishing synthetic ribbon-type active zones in a heterologous expression system
Encoding of several sensory modalities into neural signals is mediated by ribbon synapses. The synaptic ribbon tethers synaptic vesicles at the presynaptic active zone (AZ) and might act as a super-scaffold organizing AZ topography. Here we employed a synthetic biology approach to reconstitute ribbon-type AZs in HEK293 cells for probing their minimal molecular requirements and studying presynaptic Ca2+ channel clustering. Co-expressing a membrane-targeted version of the AZ-protein Bassoon and the ribbon core protein RIBEYE, we observed structures recapitulating basic aspects of ribbon-type AZs, which we call synthetic ribbons or SyRibbons. SyRibbons with Ca2+ channel clusters formed upon additional expression of CaV1.3 Ca2+ channels and RIM-binding protein 2 (RBP2), known to promote presynaptic Ca2+ channel clustering. Confocal and super-resolution microscopy along with functional analysis by patch-clamp and Ca2+-imaging revealed striking similarities and interesting differences of SyRibbons in comparison to native IHC ribbon-type AZs. In summary, we identify Ca2+ channels, RBP, membrane-anchored Bassoon, and RIBEYE as minimal components for reconstituting a basic ribbon-type AZ. SyRibbons might complement animal studies on molecular interactions of AZ proteins.

Temporal dynamics and maturation of resting-state activity in preterm infants
By interfering with the normal sequence of mechanisms serving the brain maturation, premature birth and related stress can alter perinatal experiences, with potential long-term consequences on a child's neurodevelopment. The early characterization of brain functioning and maturational changes is thus of critical interest in premature infants who are at high risk of atypical outcomes and could benefit from early diagnosis and dedicated interventions. Using high-density electroencephalography (HD-EEG), we recorded resting-state brain activity in extreme and very preterm infants at the equivalent age of pregnancy term (n=43), and longitudinally 2-months later (n=33), compared with full-term born infants (n=14). We characterized the maturation of brain activity by using microstate analysis (a method to quantify the spatiotemporal dynamics of the spontaneous transient network activity) while controlling for vigilance states. The comparison of premature and full-term infants first showed slower dynamics as well as altered spatio-temporal properties of resting-state activity in preterm infants. Maturation of functional networks between term-equivalent age and 2 months later in preterms was translated by the emergence of richer dynamics, manifested in part by faster temporal activity (shorter duration of microstates) as well as an evolution in the spatial organization of the dominant microstates. The inter-individual differences in the temporal dynamics of brain activity at term-equivalent age were further impacted by gestational age at birth and sex (with slower microstate dynamics in infants with lower birth age and in boys) but not by other considered risk factors. This study highlights the potential of the microstate approach to reveal maturational properties of the emerging resting-state network activity in premature infants.

Dysregulated Neurofluid Coupling as a New Noninvasive Biomarker for Primary Progressive Aphasia
Objective: Accumulation of pathological tau is one of the primary causes of Primary Progressive Aphasia (PPA). The glymphatic system is crucial for removing metabolic waste from the brain, whereas the underlying mechanism on the interplay between impairments in glymphatic clearance and PPA is poorly understood. Therefore, the aim of this study is to investigate the role that dysregulated macroscopic cerebrospinal fluid (CSF) movement plays in the pathology of PPA. Methods: 56 PPA patients and 94 healthy controls (HCs) participated this work. The coupling strength between blood-oxygen-level-dependent (BOLD) signals in the gray matter and CSF flow within the subarachnoid space and ventricular system was calculated by using Pearson correlation and made comparison between the two groups. Its associations with clinical characteristics including scores from Clinical Dementia Rating (CDR), Mini-Mental State Exam, Geriatric Depression Scale and with morphological measures in the hippocampus and entorhinal cortex were also quantified. Results: The PPA group exhibited decreased global BOLD and CSF coupling as compared to that of HCs, indicating impaired glymphatic functions of the patients (p = 0.006). More importantly, it was discovered that BOLD-CSF coupling of PPA group rather than that of the HCs demonstrated significant correlations with the CDR scores (p = 0.04), hippocampal volume (p = 0.005), and entorhinal cortex thickness (p = 0.04). Interpretation: The measured decoupling between global brain activation (hemodynamic response) and CSF flow and its association with symptomology and brain structural changes in PPA revealed the glymphatic dysregulation in PPA. Herein, this evidence supports the potential role of BOLD-CSF coupling as a noninvasive biomarker for the detection and prediction of PPA.

Comprehensive evaluation of pipelines for diagnostic biomarkers of major depressive disorder using multi-site resting-state fMRI datasets
The objective diagnostic and stratification biomarkers developed with resting-state functional magnetic resonance imaging (rs-fMRI) data are expected to contribute to more effective treatment for mental disorders. Unfortunately, there are currently no widely accepted biomarkers, partially due to the large variety of analysis pipelines for developing them. In this study we comprehensively evaluated analysis pipelines using a large-scale, multi-site fMRI dataset for major depressive disorder (MDD) (1162 participants from eight imaging sites). We explored the combinations of options in four subprocesses of analysis pipelines: six types of brain parcellation, four types of estimations of functional connectivity (FC), three types of site difference harmonization, and five types of machine learning methods. 360 different MDD diagnostic biomarkers were constructed using the SRPBS dataset acquired with unified protocols (713 participants from four imaging sites) as a discovery dataset and evaluated with datasets from other projects acquired with heterogeneous protocols (449 participants from four imaging sites) for independent validation. To identify the optimal options regardless of the discovery dataset, we repeated the same procedure after swapping the roles of the two datasets. We found pipelines that included Glasser's parcellation, tangent-covariance, no harmonization, and non-sparse machine learning methods tended to result in high classification performance. The diagnosis results of the top 10 biomarkers showed high similarity, and weight similarity was also observed between eight of the biomarkers, except two that used both data-driven parcellation and FC computation. We applied the top 10 pipelines to the datasets of other mental disorders (autism spectral disorder: ASD and schizophrenia: SCZ) and eight of the ten biomarkers showed sufficient classification performances for both disorders, except two pipelines that included Pearson correlation, ComBat harmonization and random forest classifier combination.

Normative growth modeling of brain morphology reveals neuroanatomical heterogeneity and biological subtypes in children with ADHD
Background Neuroimaging studies suggest substantial individual heterogeneity in brain phenotypes in attention-deficit/hyperactivity disorder (ADHD). However, how these individual-level brain phenotypes contribute to the identification of ADHD biotypes and whether these biotypes have different treatment outcomes and neurobiological underpinnings remain largely unknown. Methods We collected multisite, high-quality structural magnetic resonance imaging data from 1,006 children aged 6-14 years, including 351 children with ADHD and 655 typically developing children. Normative growth models of cortical thickness were established for 219 regions in the typically developing children. Individual-level deviations from these normal references were quantified and clustered to identify ADHD biotypes. We validated the replicability and generalizability of the ADHD biotypes using two independent datasets and evaluated the associations of the biotypes with symptomatic, cognitive, and gene expression profiles, as well as follow-up treatment outcomes. Findings No more than 10% of children with ADHD had extreme deviations in cortical thickness in a single region, suggesting high heterogeneity among individuals with ADHD. On the basis of the brain deviation maps, we discovered two robust ADHD biotypes, an infra-normal subtype with cortical thinning associated with ADHD symptoms and a supranormal subtype with cortical thickening associated with cognition. Patients with the infra-normal subtype responded better to methylphenidate than to atomoxetine, although both subtypes showed treatment efficacy. Brain deviations in the infra-normal subtype were explained by the expression levels of genes enriched in presynaptic and axonal development and polygenic risk of ADHD. Interpretation We identified anatomically distinct, clinically valuable, and biologically informed ADHD subtypes, providing insight into the neurobiological basis of clinical heterogeneity and facilitating a personalized medication strategy for ADHD patients.