Brain Communications

Cognitive Motor Dissociation (CMD) describes a condition whereby brain injury patients can demonstrate response to command through advanced electrophysiology and imaging assessments but are unable to do so through in standard, behavior-based, clinical assessments. Rightfully, significant emphasis has been placed on the fact that despite a similar behavioral phenotype, patients with CMD show better outcomes than patients without CMD. Yet, this finding is not overly surprising when considering that patients with CMD might just be minimally conscious state "plus" patients (MCS+; i.e., patients capable of response to command) who were misdiagnoses due to the known limitations of behavioral assessments in the presence of sensory, cognitive, or motor comorbidities. The present work brings together 131 DOC patients, from two separate longitudinal studies, to assess whether patients able demonstrate response to command via brain responses but not behavioral responses (i.e., CMD patients) are "just" misdiagnosed MCS+ patients, in terms of short-term outcomes, or whether they represent a separate diagnostic entity. Robust general linear modelling reveals that, while CMD patients show greater short-term gains than patients with no evidence of CMD, consistent with prior work, these gains are not different from those seen in patients who can demonstrate response to command behaviorally (i.e., MCS+ patients). This pattern of results remains unchanged when separately analyzing Vegetative State (VS; i.e., entirely unresponsive) and Minimally Conscious State "minus" patients (MCS-; i.e., patients only able to show non-language-mediated response) with and without CMD, and when restricting analyses to traumatic brain injury patients only. These findings suggest that, at least in terms of short-term outcomes, patients with CMD are not meaningfully different from MCS+ patients. Rather, CMD patients are best understood as MCS+ patients who were misdiagnosed likely due to the well-known limitations of behavioral assessments in the presence of comorbidities affecting sensory input, cognitive processing, and/or motor output. These results thus support the suggestion by the European Union practice guidelines to assign diagnoses based on the highest level of response obtained in a patient across behavioral and non-behavioral assessments, as well as the use of advanced assessments not only in behaviorally VS patients, consistent with the US guidelines, but also in MCS- patients. Finally, from a nosological perspective, these findings suggest that patients with CMD might best be described as "MCS+ patients with CMD," to convey at once their true level of consciousness (i.e., MCS+) and the presence of motor output limitations (i.e., CMD).

Numerous large-scale epidemiological studies investigating the trajectory of cognitive recovery after ischemic stroke have presented data suggesting an immediate drop in cognition acutely post-stroke followed by persistent, accelerated decline over time when averaged as a group. We sought to further examine this trend, speculating that the average persistent decline may be a reflection of two subgroups with vastly different prognoses: 1) a minority experiencing decline secondary to neurodegenerative processes like vascular dementia and Alzheimers disease, and 2) a majority without marked progressive brain atrophy who typically see improvement. Our team thus investigated atrophys association with language recovery, hypothesizing that declining naming performance in the year after left hemisphere ischemic stroke would be correlated to atrophy of the contralesional hemisphere. We postulated that volume loss within the lesioned hemisphere would be less informative due to separate confounding processes related to the stroke itself like Wallerian degeneration and encephalomalacia. Participants (n=72; M[SD] age=60[11]) in a longitudinal cohort study of language following left hemisphere ischemic stroke were included if they completed an MRI both acutely and chronically (either 6- or 12-months post-stroke). Naming performance was assessed using the Boston Naming Test, stroke volumes were extracted from acute imaging, and atrophy was measured as the monthly percent change in hemispheric volume from baseline to chronic scan for each individual. Pearsons correlations were calculated to determine the relationship between lesion volume and atrophy along with atrophy and change in Boston Naming Test score. Lesion volume was negatively correlated to the monthly percent change of volume in the left (ipsilesional) hemisphere (r=-0.48; p<0.0001) but was not correlated to rate of right (contralesional) hemisphere volume loss. While there was no clear relationship between atrophy of the left hemisphere and language recovery, we found that volume changes of greater negative magnitude within the right hemisphere (increased atrophy) were associated with worse functional recovery of language (r=0.38; p=0.0025). By showing that atrophy of the right hemisphere was not significantly impacted by left hemisphere lesion size, we suggest that accelerated volume loss in the non-lesioned hemisphere after stroke may be indicative of a separate pathology. We then go on to support this claim with behavioral data showing that greater rates of volume loss within the non-lesioned hemisphere were associated with poorer naming recovery. Together, these findings imply that contralesional atrophy after stroke may have negative implications for recovery and could serve as a useful imaging signature for separate neurodegenerative processes.

The ability of the brain to actively cope with neuropathological insults is known as neural compensation. It explains the delayed appearance of cognitive symptoms in neurodegenerative diseases. In contrast to the neural signature of compensation, its cognitive counterpart is largely unknown due to the difficulty of identifying cognitive dysfunctions concealed by compensation mechanisms. We combined computational modelling and neuroanatomical analysis to explore cognitive compensation. We used Huntingtons disease (HD) as a genetic model of neurodegenerative disease allowing to study compensation in premanifest mutation carriers (preHDs) free from overt cognitive deficits despite incipient brain atrophy. Twenty preHDs, 28 HD patients and 45 controls performed a discrimination task. We investigated the processes underlying cognitive compensation using drift diffusion models. They assume that the discrimination process relies on the accumulation of evidence at a certain rate and terminates when a response threshold is reached. HD patients performances were lower than controls and explained by a higher response threshold and a lower accumulation rate compared to controls. PreHDs performed similarly to controls but had a response threshold between those of controls and HD patients. This nascent increase in response threshold predicted the accumulation rate, which was faster than controls. This suggests that the higher accumulation rate conceals the nascent deficit in response threshold corroborating the capacity of the brain to resist neuropathological insults in preHDs. The higher accumulation rate was associated with parietal hypertrophy in mutation carriers, and with higher hippocampal volumes in preHDs suggesting that cognitive compensation may rely on attentional capacities. Significance statementEnhancing mechanisms compensating brain degeneration in neurodegenerative diseases might allow to delay their onset and progression. Yet, the cognitive mechanisms of compensation remain to be identified. In order to explore this issue, we used Huntingtons disease as a genetic model of neurodegenerative diseases and combined computational modelling (drift diffusion models) and neuroanatomical data analysis. In the early stage of the disease, before the appearance of overt cognitive symptoms, we showed the involvement of the left superior parietal cortex and hippocampus in maintaining normal behavioural performances. This suggests that attention is used to compensate for brain atrophy early in the disease. This work describes promising means of measuring and understanding compensation mechanisms in neurodegenerative diseases and might help developing new therapies.

ObjectivesInsight is an important predictor of quality of life in Huntingtons disease and other neurodegenerative conditions. However, estimating insight with traditional methods such as questionnaires is challenging and subject to limitations. This cross-sectional study experimentally quantified metacognitive insight into cognitive performance in Huntingtons disease gene-carriers. MethodsWe dissociated perceptual decision-making performance and metacognitive insight into performance in healthy controls (n=29), premanifest (n=19) and early-manifest (n=10) Huntingtons disease gene-carriers. Insight was operationalised as the degree to which a participants confidence in their performance was informative of their actual performance (metacognitive efficiency) and estimated using a computational model (HMeta-d). ResultsWe found that pre and early-manifest Huntingtons disease gene-carriers were impaired in making perceptual decisions compared to controls. Gene-carriers required more evidence in favour of the correct choice to achieve similar performance and perceptual impairments were increased in those with manifest disease. Surprisingly, despite marked perceptual impairments, Huntingtons disease gene-carriers retained metacognitive insight into their perceptual performance. This was the case after controlling for confounding variables and regardless of disease stage. ConclusionWe report for the first time a dissociation between impaired cognition and intact metacognition (trial-by-trial insight) in the early-stages of a neurodegenerative disease. This unexpected finding contrasts with the prevailing assumption that cognitive deficits are associated with impaired insight. Future studies should investigate how intact metacognitive insight could be used by some early Huntingtons disease gene-carriers to positively impact their quality of life.

The substantia nigra and locus coeruleus are among the first brain regions to degenerate in Parkinsons disease. This has important implications for early cognitive deficits as these nuclei are sources of ascending neuromodulators (i.e., dopamine and noradrenaline) that support various cognitive functions like learning, memory, and executive function. However, because most studies of the relationship between patterns of degeneration and cognition have either studied these neuromodulator systems in isolation or studied specific cognitive domains in isolation, it is unknown if degeneration in the substantia nigra and degeneration in the locus coeruleus independently and selectively contribute to different cognitive deficits in Parkinsons disease. To address this gap, we tested people with Parkinsons disease and older adults on tasks of positive reinforcement learning, attention/working memory, executive function, and memory to measure performance in domains of cognition specifically thought to be related to dopaminergic and noradrenergic function. Participants also underwent neuromelanin-sensitive magnetic resonance imaging which provides a measure of degeneration of dopamine neurons in the substantia nigra and of noradrenergic neurons in the locus coeruleus. Brain-behaviour relationships were evaluated by separate linear regressions predicting cognitive performance in each domain from substantia nigra and locus coeruleus neuromelanin signal intensities controlling for age, sex, and education. As expected, Parkinsons disease patients had significantly slower learning from positive feedback and lower performance on tests of attention/working memory, executive function, and memory than controls. Parkinsons patients also had lower neuromelanin signal intensity in the substantia nigra and locus coeruleus. Examining brain-behaviour relationships, we found that reduced neuromelanin signal in the substantia nigra in Parkinsons disease patients was independently associated with impaired positive reinforcement learning, controlling for changes in the locus coeruleus, but was not associated with other domains of cognition. In contrast, reduced neuromelanin signal in the locus coeruleus was independently associated with impairments in attention/working memory and executive function, controlling for changes in the substantia nigra, but not with reinforcement learning performance. These results show that substantia nigra degeneration and locus coeruleus degeneration independently and selectively contribute to cognitive deficits and therefore suggests that individual differences in the degree of neurodegeneration in these nuclei could explain the significant heterogeneity that exists in the cognitive and behavioural manifestations of Parkinsons disease. These findings also highlight the potential value of leveraging known brain-behaviour relationships to develop performance-based measures of cognition that reflect underlying patterns of neurodegeneration.

Hemiparesis is a common consequence of stroke to the primary motor system. Previous studies suggested that damage to additional brain areas might play a causal role in the occurrence and severity of hemiparesis and its recovery. Knowledge of these regions might be applied in the creation of imaging biomarkers for motor outcome prediction if lesion information from such areas carries predictive value. We assessed acute and chronic paresis of the upper limb in 102 patients with unilateral stroke. In a first experiment, the neural correlates of acute and chronic upper limb paresis were mapped by lesion behaviour mapping. Following the same approach, a lesion biomarker of corticospinal tract (CST) damage was mapped. This analysis served as an artificial control condition as the biomarker, by definition, is only causally related to damage of the CST. Mapping acute or chronic upper limb paresis implicated areas outside of the primary motor system. Likewise, mapping the CST lesion biomarker implicated several areas outside of the CST with high correspondence to areas associated with upper limb paresis. Damage to areas outside of the primary motor system thus might, to some degree, not play a causal role in hemiparesis. In a second experiment, we showed that lesion information from these areas outside of the primary motor system can be used to predict motor outcome. This was even the case for the CST lesion biomarker. Although the only causal source underlying the CST lesion biomarker was damage to the CST, lesion information that mainly included non-CST regions was able to predict the biomarker (non-significantly) better than information taken from only the CST itself. These findings suggest that simple theory-based biomarkers or qualitative rules to infer post-stroke outcome from imaging data might perform sub-optimally, as they do not consider the complexity of lesion data. Instead, high-dimensional models with data-driven feature selection strategies might be required.

RationaleApathy is a common symptom in many neurological and psychiatric conditions, associated with poor prognosis and increased caregiver burden. There are currently no proven treatments, in part due to a lack of mechanistic understanding. We have proposed a novel framework for apathy, based on the failure of active inference due to imprecise priors on the outcome of actions. The loss of precision on action outcomes causes apathy by reducing the expected difference between the state of the world following action versus non-action. Here we test the hypothesis that the loss of prior precision on action outcomes is reversible and mediated neuronally by GABAergic gain on the superficial pyramidal neurons in a prefrontal-motor decision-making hierarchy. We test this in a healthy cohort and people with two syndromes associated with frontotemporal lobar degeneration. MethodsTwenty healthy controls, twenty people with behavioural variant frontotemporal dementia (bvFTD) and twenty people with progressive supranuclear palsy (PSP) took part in a randomised placebo-controlled double-blind trial using zolpidem, an established GABAA agonist. This study was registered with ISRCTN registry (ISRCTN10616794). We use the Goal Prior Assay task and dynamic causal modelling of MEG resting-state data to explore the cognitive and neural concomitants of prior precision, and the effect of GABAergic regulation on both prior precision and superficial pyramidal gain. Apathy was primarily assessed with the Apathy Evaluation Scale (Self and Carer). Principal analyses were conducted using Bayesian statistics, supplemented by classical frequentist tests. ResultsForty-three participants (20 controls and 23 patients) were included in the final analysis. We found strong evidence for a difference in measures of apathy (B>1000, p<0.001) and prior precision (B=20.4, p<0.01) between healthy controls and people with bvFTD and PSP. This difference in prior precision was not found in the drug condition (B=0.86, p=0.11). There was strong evidence of a correlation between apathy and prior precision across groups (B>100, p<0.001). Dynamic causal modelling of MEG resting-state data confirmed reductions in gain on the prefrontal superficial pyramidal neurons in patients. The prefrontal superficial pyramidal gain was partially restored on zolpidem and linked to participants prior precision on their action outcomes. ConclusionWe confirm that apathy in conditions associated with frontotemporal lobar degeneration is underwritten by a reduction in prior precision on action outcomes, mediated by reduced synaptic gain of prefrontal superficial pyramidal neurons. GABAergic regulation using zolpidem partially restores prior precision by acting on this gain and reinstating neuronal message-passing within the prefrontal-motor decision-making hierarchy.

Temporal measures, such as time from diagnosis or symptom onset are often used to track disease severity in neurodegenerative diseases. Due to variations in symptom awareness, clinical presentation timing, diagnostic delays, and disease progression rates, these temporal proxies introduce substantial variance and bias, making it very difficult to map progression clearly and accurately, and to severity-match across contrastive patient groups. To address this challenge, we explored a data-driven approach to derive a transdiagnostic severity metric that is independent of time and, instead, treats temporal metrics as observed, dependent data. We analysed data from the Genetic Frontotemporal Dementia Initiative (GENFI 1 and 2). We entered neuropsychological scores for symptomatic individuals including any visits prior to conversion from at-risk to symptomatic (n = 265, 522 visits) in an unrotated principal component analysis to derive a transdiagnostic phenotype-severity model. A single component emerged (Kaiser-Meyer-Olkin = 0.92), explaining 65% of the variance, with all neuropsychological assessments loading highly. This global severity component fitted the data equally well across genetically or clinically defined groups, as well as severity levels. The severity measures validity was supported by a clear relationship with the Clinical Dementia Rating scale, and its stability was confirmed when a much broader range of neuropsychological and behavioural measures were included. Additionally, the severity score accounted for a high portion of the total variance in neuropsychological test scores, substantially more than the low proportion accounted for by standard temporal measures. To derive a time-efficient sub-battery, we demonstrated that three neuropsychological assessments (Digit Symbol, Verbal fluency (letters) and Trail Making Test- Part B were able to explain the majority of unique variance in cognitive severity. Finally, by treating time as an observed dependent variable, we showed that the baseline velocity (change in severity measure over time) varied by genetic group, with progranulin mutation carriers being the fastest. This data-driven approach provides an objective, precise measure of disease severity and progression, and it may shed new light on when clinical heterogeneity reflects distinct subtypes rather than differences in disease stage.

Molecular mechanisms of neuropsychiatric disorders are challenging to study in human brain. For decades, the preferred model has been to study postmortem human brain samples despite the limitations they entail. A recent study generated RNA sequencing data from biopsies of prefrontal cortex from living patients with Parkinsons Disease and compared gene expression to postmortem tissue samples, from which they found vast differences between the two. This led the authors to question the utility of postmortem human brain studies. Through re-analysis of the same data, we unexpectedly found that the living brain tissue samples were of much lower quality than the postmortem samples across multiple standard metrics. We also performed simulations that illustrate the effects of ignoring RNA degradation in differential gene expression analyses, showing the effects can be substantial and of similar magnitude to what the authors find. For these reasons, we believe the authors conclusions are unjustified. To the contrary, while opportunities to study gene expression in the living brain are welcome, evidence that this eclipses the value of postmortem analyses is not apparent.

Lesion network mapping (LNM) and related techniques have been used in over 200 studies, primarily to test whether anatomically distributed lesions that cause the same symptom fall within a common brain network. A recent article1 challenges the specificity and validity of this technique, suggesting that lesion network maps primarily reflect intrinsic properties of the normative connectome rather than lesion-symptom relationships. However, the data and procedures in van den Heuvel et al. do not reflect those used in most LNM studies. Further, the main conclusions were based on similarity between maps, but similarity does not imply the absence of meaningful differences. In contrast, LNM provides evidence for meaningful differences using specificity testing. Exemplary analyses of 1090 lesion locations from 34 prior LNM studies do not support van den Heuvels concerns and confirm the lesion-deficit specificity of LNM. While we encourage further methodological investigation, the analyses of van den Heuvel et al. do not invalidate prior LNM findings or future applications.

Background and ObjectivesConverging evidence hints at neurodevelopmental effects in people at risk of genetic frontotemporal dementia (FTD), including associations between FTD-causing mutations and neurodevelopmental disorders, and differences in young adult mutation carriers compared to familial non-mutation carriers in total intracranial volume (TIV) and cognition. We aimed to investigate TIV and educational attainment differences between adult mutation carriers and familial non-mutation carriers, as measures of the structural and functional neurodevelopmental effects of the FTD-causing genetic mutations. MethodsThis cross-sectional cohort study was facilitated through the FTD Prevention Initiative (FPI). Participants, aged 18 to 86 years, were pathogenic mutation carriers of GRN, MAPT, or C9orf72, or familial non-carriers. ANCOVAs were computed per gene to compare outcome means for the main effect of group by carrier status, while controlling for birth decade, sex, and visit site (to account for unique scanners and educational systems). Pearsons correlations were used to examine associations between TIV and education. ResultsNine-hundred two mutation carriers (mean{+/-}SD; age=50.0{+/-}13.2 years, sex=55% female, n(GRN)=298, n(MAPT)=187, n(C9orf72)=417) were compared to 532 familial non-carriers (age=48.0{+/-}12.9 years, sex=58% female, n(GRN)=201, n(MAPT)=114), n(C9orf72)=217). Consistent with prior findings in young adults, GRN carriers showed larger TIV compared to familial non-carriers (95% confidence interval [CI]=1431994-1457123, p=0.049, 2p=0.008). Larger TIV correlated with higher years of education in GRN carriers (95% CI=0.01-0.24, r(295)=0.12, p=0.03) and GRN non-carriers (95% CI=0.08-0.34, r(198)=0.21, p=0.002). MAPT carriers demonstrated smaller TIV than non-carriers (95% CI=1417819-1450628, p=0.039, 2p=0.02). Models with C9orf72 and education as outcome variables did not reveal significant differences. DiscussionIn support of the neurodevelopmental hypothesis of FTD, GRN and MAPT mutations are linked to likely structural neurodevelopmental changes in TIV, some of which correlate to years of education. These findings motivate further research to identify mechanisms by which FTD mutations influence neurodevelopment and ascertain their suitability as targets for interventions.

Parkinsons disease and progressive supranuclear palsy (PSP) both impair response inhibition, exacerbating impulsivity. Inhibitory control deficits vary across individuals, and have been linked with worse prognosis and lack of improvement on dopaminergic therapy. Motor and cognitive control are associated with noradrenergic innervation of the cortex, arising from the locus coeruleus noradrenergic system. Here we test the hypothesis that loss of structural integrity of the locus coeruleus explains response inhibition deficits in progressive supranuclear palsy and Parkinsons disease. This cross-sectional observational study recruited 24 people with idiopathic Parkinsons disease, 14 with PSP-Richardsons syndrome, and 24 age- and sex-matched controls. All participants undertook a stop-signal task and ultrahigh field 7T-magnetic transfer weighted imaging of the locus coeruleus. Hierarchical Bayesian estimation of the parameters of race models of go-versus stop-decisions was used to quantify the cognitive processes of response inhibition. We tested the multivariate relationship between locus coeruleus integrity and model parameters using partial least squares. Both disorders impaired response inhibition at the group level. Progressive supranuclear palsy caused a distinct pattern of abnormalities in inhibitory control, relative to Parkinsons disease and healthy controls, with a paradoxically reduced threshold for go responses, but longer non-decision times, and more lapses of attention. The variation in response inhibition correlated with variation in the integrity of the locus coeruleus, across participants in both clinical groups. Structural imaging of the locus coeruleus, coupled with behavioural modelling in parkinsonian disorders, confirms that locus coeruleus integrity is associated with response inhibition and its degeneration contributes to neurobehavioural changes. The noradrenergic system is therefore a promising target to treat impulsivity in these conditions. The optimisation of noradrenergic treatment is likely to benefit from stratification according to locus coeruleus integrity.

Repurposed noradrenergic drugs have been proposed to treat neuropsychiatric symptoms in Parkinsons disease and related conditions. While there is evidence that these drugs can be beneficial for cognition in selected patients, questions remain about their cardiovascular effects. Here we tested whether heart rate variability (HRV) is altered in people with Parkinsons disease, following a single-dose challenge with the noradrenaline reuptake inhibitor atomoxetine (40 mg, oral). Consistent with previous work, our cohort of people with idiopathic Parkinsons disease (n=15) had lower HRV than healthy controls (n=22). Decreased HRV in people with Parkinsons disease was associated with reduced integrity of the caudal locus coeruleus, measured using neuromelanin-sensitive ultra-high field 7T magnetic resonance imaging. Following a randomised double-blind placebo-controlled crossover challenge in the Parkinsons disease group, short-term resting HRV was not significantly altered following atomoxetine. Using Bayesian statistical inference, we demonstrated confidence in the preservation of HRV across measures in the time, frequency, and non-linear domains. Our findings are in favour of a safe cardiovascular profile for atomoxetine in Parkinsons disease, further supporting noradrenergic modulation as a viable treatment strategy for neuropsychiatric symptoms in Parkinsons disease and related disorders.

Parkinsons disease (PD) is a progressive neurodegenerative disorder which causes debilitating symptoms in both the motor and cognitive domains. The neurophysiological markers of PD include oscillopathies such as diffuse neural oscillatory slowing, dysregulated beta band activity, and changes in interhemispheric functional connectivity; however, the relative importance of these markers as determinants of disease status is not clear. In this study, we used resting state magnetoencephalography data (n = 199 participants, 78 PD, 121 controls) from the open OMEGA repository to investigate changes in spectral power and functional networks in PD. Using a Contrast of Parameter Estimates (COPE) approach, we modelled the effects of PD while controlling for population-level confounds (age, sex, brain volume). Permutation testing revealed highly significant increases in theta (p=0.0001) and decreases in gamma band spectral power (p=0.0001). Building on the group contrast results, we investigated the ability of source-resolved MEG data to distinguish PD from healthy controls. Our approach uses a Partial Least Squares (PLS)-based classifier to find linear combinations of MEG features which independently predict PD. We found MEG-based predictions to be highly sensitive and specific, reaching an optimal AUC-ROC of 0.87 {+/-} 0.04 using a model including spectral power features with 4 independent PLS components, compared to 0.68 {+/-} 0.04 when using functional connectivity. Interpretation of the model weights suggests that oscillatory slowing can be separated into independent posterior theta and global diffuse delta components that can robustly identify individual cases of PD with a high degree of accuracy. This suggests MEG can reveal dissociable, complementary neural processes which contribute to PD.

BackgroundDeep Brain Stimulation (DBS) is an established and effective treatment for patients with Parkinsons disease (PD). Over recent decades, several brain targets have been explored for DBS, including the subthalamic nucleus (STN) and the globus pallidus internus (GPi). However, the relative efficacy of these targets in controlling tremor remains a subject of debate. This study aimed to systematically compare tremor outcomes between STN-DBS and GPi-DBS in patients with PD. MethodsA prospectively registered protocol guided a comprehensive search of PubMed, Embase, Scopus and Web of science for randomized and controlled studies comparing tremor outcomes between STN-DBS and GPi-DBS. Standardized mean differences (Hedges g) in tremor reduction were extracted and synthesized using a random-effects model. The primary outcomes included the overall magnitude of tremor improvement, inter-study heterogeneity, and inter-individual variability in short-term response. ResultsBoth STN-DBS and GPi-DBS yielded substantial and durable tremor reductions, typically ranging from 70% to 90% improvement from baseline. Meta-analytic pooling revealed no significant long-term difference between targets (Hedges g = -0.08; 95% CI, -0.53 to 0.38; p = 0.74), with minimal inter-study heterogeneity (I{superscript 2} = 0%). However, short-term postoperative data indicated a modest but consistent early advantage for STN-DBS in achieving faster tremor relief. Importantly, clinical follow-up findings revealed considerable variability in the magnitude and timing of tremor improvement across individual patients, independent of target. ConclusionsSTN-DBS and GPi-DBS are equally effective for long-term tremor control in PD. Nevertheless, the transient early benefit observed with STN stimulation and the pronounced individual variability in treatment response emphasize the importance of personalized DBS planning. Tailoring target selection and programming to patient-specific clinical profiles may optimize both short- and long-term outcomes

Revised AbstractThe left supramarginal gyrus (LSMG) may mediate attention to memory, and gauge memory state and performance. We performed a secondary analysis of 142 verbal delayed free recall experiments, in patients with medically-refractory epilepsy with electrode contacts implanted in the LSMG. In 14 of 142 experiments (in 14 of 113 patients), the cross-validated convolutional neural networks (CNNs) that used 1-dimensional(1-D) pairs of convolved high-gamma and beta tensors, derived from the LSMG recordings, could label recalled words with an area under the receiver operating curve (AUROC) of greater than 60% [range: 60-90%]. These 14 patients were distinguished by: 1) higher amplitudes of high-gamma bursts; 2) distinct electrode placement within the LSMG; and 3) superior performance compared with a CNN that used a 1-D tensor of the broadband recordings in the LSMG. In a pilot study of 7 of these patients, we also cross-validated CNNs using paired 1-D convolved high-gamma and beta tensors, from the LSMG, to: a) distinguish word encoding epochs from free recall epochs [AUC 0.6-1]; and distinguish better performance from poor performance during delayed free recall [AUC 0.5-0.86]. These experiments show that bursts of high-gamma and beta generated in the LSMG are biomarkers of verbal memory state and performance.

Repetitive head impacts (RHI) are associated with an increased risk of developing various neurodegenerative disorders, such as Alzheimers disease (AD), Parkinsons disease (PD), and most notably, chronic traumatic encephalopathy (CTE). While the clinical presentation of AD and PD is well established, CTE can only be diagnosed post-mortem. Therefore, a distinction can be made between the pathologically defined CTE and RHI-related functional or structural brain changes (RHI-BC) which may result in CTE. Unfortunately, there are currently no accepted biomarkers of CTE nor RHI-BC, a major hurdle to achieving clinical diagnoses. Interestingly, speech has shown promise as a potential biomarker of both AD and PD, being used to accurately classify individuals with AD and PD from those without. Given the overlapping symptoms between CTE, RHI-BC, PD and AD, we aimed to determine if speech could be used to identify individuals with a history of RHI from those without. We therefore created the Verus dataset, consisting of 13 second voice recordings from 605 professional fighters (RHI group) and 605 professional athletes in non-contact sports (control group) for a total of 1210 recordings. Using a deep learning approach, we achieved 85% accuracy in detecting individuals with a history of RHI from those without. We then used our model trained on the Verus dataset to fine-tune on publicly available AD and PD speech datasets and achieved new state-of-the-art accuracies of 84.99% on the AD dataset and 89% on the PD dataset. Finding a biomarker of CTE and RHI-BC that presents early in disease progression is critical to improve risk management and patient outcome. Our study is the first we are aware of to investigate speech as such a candidate biomarker of RHI-BC.

Chronic motor impairments are a leading cause of disability after stroke. Previous studies have predicted motor outcomes based on the degree of damage to predefined structures in the motor system, such as the corticospinal tract. However, such theory-based approaches may not take full advantage of the information contained in clinical imaging data. The present study uses data-driven approaches to predict chronic motor outcomes after stroke and compares the accuracy of these predictions to previously-identified theory-based biomarkers. Using a cross-validation framework, regression models were trained using lesion masks and motor outcomes data from 789 stroke patients (293 female/496 male) from the ENIGMA Stroke Recovery Working Group (age 64.9{+/-}18.0 years; time since stroke 12.2{+/-}0.2 months; normalised motor score 0.7{+/-}0.5 (range [0,1]). The out-of-sample prediction accuracy of two theory-based biomarkers was assessed: lesion load of the corticospinal tract, and lesion load of multiple descending motor tracts. These theory-based prediction accuracies were compared to the prediction accuracy from three data-driven biomarkers: lesion load of lesion-behaviour maps, lesion load of structural networks associated with lesion-behaviour maps, and measures of regional structural disconnection. In general, data-driven biomarkers had better prediction accuracy - as measured by higher explained variance in chronic motor outcomes - than theory-based biomarkers. Data-driven models of regional structural disconnection performed the best of all models tested (R2 = 0.210, p < 0.001), performing significantly better than predictions using the theory-based biomarkers of lesion load of the corticospinal tract (R2 = 0.132, p< 0.001) and of multiple descending motor tracts (R2 = 0.180, p < 0.001). They also performed slightly, but significantly, better than other data-driven biomarkers including lesion load of lesion-behaviour maps (R2 =0.200, p < 0.001) and lesion load of structural networks associated with lesion-behaviour maps (R2 =0.167, p < 0.001). Ensemble models - combining basic demographic variables like age, sex, and time since stroke - improved prediction accuracy for theory-based and data-driven biomarkers. Finally, combining both theory-based and data-driven biomarkers with demographic variables improved predictions, and the best ensemble model achieved R2 = 0.241, p < 0.001. Overall, these results demonstrate that models that predict chronic motor outcomes using data-driven features, particularly when lesion data is represented in terms of structural disconnection, perform better than models that predict chronic motor outcomes using theory-based features from the motor system. However, combining both theory-based and data-driven models provides the best predictions.

The mechanisms of aphasia recovery following left-hemisphere stroke remain debated. Two broad hypotheses have been proposed for how recovery occurs when specialized systems, such as the language system, are affected by brain damage: i) recovery depends on the remaining components of the language system; and ii) recovery depends on functional remapping in brain areas outside of the language system. A key candidate for such takeover of language function is the Multiple Demand (MD) system--an extensive bilateral network that supports executive functions and is associated with the ability to flexibly adapt to task goals. The theoretical premise is that this system is capable of a wide range of cognitive tasks and can potentially be repurposed for language when specialized resources are no longer sufficient. We used precision functional MRI to evaluate these two hypotheses about aphasia recovery in 37 individuals (mean age = 58.3, SD = 8.4) with chronic aphasia due to a single left-hemisphere stroke, along with 38 age-matched controls (mean age = 61.6, SD = 9.2). Participants performed extensively validated functional localizers to identify the language network and the MD network within individuals. Participants with aphasia additionally completed extensive behavioral assessments that evaluated linguistic and executive skills. We first examined responses during language processing--audio-visual speech comprehension and reading--in each of the two networks, and then we related activity and functional connectivity measures from the two networks to linguistic ability. Our results do not support the hypothesis of drastic reorganization of the language system in the form of co-opting parts of the MD system in chronic aphasia. First, the language network and the MD network remain robustly dissociated: the language network responds strongly and selectively to language across modalities (left-hemisphere language regions: pFDR < 0.003), and no MD region shows increased activation during language comprehension relative to controls (pFDR > 0.24). Second, functional connectivity analyses reveal no evidence for increased integration between the two networks during language processing. Third, linguistic ability, as measured by an extensive behavioral battery of tests, is associated with the strength of activity and functional connectivity within the language network, but not within the MD network. Although we cannot rule out a role for the MD network in aphasia recovery during the acute and subacute phases or in more severely impaired patients, it appears that during the chronic phase, language comprehension relies on the same specialized network as prior to the injury.

Magnetic resonance spectroscopy (MRS) and functional MRI (fMRI), related through common biophysical bases, provide complementary information about brain function. The link between MRS and fMRI measures is of interest, especially in the ultra-rare, metabolic disease late-onset GM2 gangliosidosis (LOGG). Imaging studies on LOGG have been few and far between, with cerebellar atrophy and neurochemical impairments being the most prominent findings. However, it remains unknown as to how these neurochemical aberrations relate to neurofunctional characteristics. The goal of this study (7 LOGG, 7 age/sex matched controls) was to assess the relationship between MRS concentrations and fMRI measures derived from the same MRS ROI (cerebellum, thalamus, precuneus) in LOGG. To quantify the communication between MRS regions and rest of the brain, we employed graph measures estimated from resting-state fMRI functional connectivity. We found that one such measure, local efficiency, which quantifies the aggregate relationship between a MRS region and rest of the brain, was significantly associated with N-acetylaspartate (NAA) in the cerebellum and thalamus (p<0.05, FDR corrected). Poorer neuronal health, neuronal loss (NAA), and neuroinflammation (myo-inositol) were related to poorer cerebellum-brain communication. Likewise, reduced thalamus-brain communication was also associated with poorer neuronal health and longer disease duration (p=0.002). These findings hint at a model of impaired neurochemical concentrations in these regions, leading to aberrant communication between them and rest of the brain, which may exacerbate disease progression. Future research must replicate these findings in larger cohorts, and further investigate such abnormalities in the cerebellum, thalamus and precuneus in this ultra-rare neurological disease.