Dimensionality reduction beyond neural subspaces with slice tensor component analysis.

Recent work has argued that large-scale neural recordings are often well described by patterns of coactivation across neurons. Yet the view that neural variability is constrained to a fixed, low-dimensional subspace may overlook higher-dimensional structure, including stereotyped neural sequences or slowly evolving latent spaces. Here we argue that task-relevant variability in neural data can also cofluctuate over trials or time, defining distinct 'covariability classes' that may co-occur within the same dataset. To demix these covariability classes, we develop sliceTCA (slice tensor component analysis), a new unsupervised dimensionality reduction method for neural data tensors. In three example datasets, including motor cortical activity during a classic reaching task in primates and recent multiregion recordings in mice, we show that sliceTCA can capture more task-relevant structure in neural data using fewer components than traditional methods. Overall, our theoretical framework extends the classic view of low-dimensional population activity by incorporating additional classes of latent variables capturing higher-dimensional structure.

A practical guide for studying human behavior in the lab.

In the last few decades, the field of neuroscience has witnessed major technological advances that have allowed researchers to measure and control neural activity with great detail. Yet, behavioral experiments in humans remain an essential approach to investigate the mysteries of the mind. Their relatively modest technological and economic requisites make behavioral research an attractive and accessible experimental avenue for neuroscientists with very diverse backgrounds. However, like any experimental enterprise, it has its own inherent challenges that may pose practical hurdles, especially to less experienced behavioral researchers. Here, we aim at providing a practical guide for a steady walk through the workflow of a typical behavioral experiment with human subjects. This primer concerns the design of an experimental protocol, research ethics, and subject care, as well as best practices for data collection, analysis, and sharing. The goal is to provide clear instructions for both beginners and experienced researchers from diverse backgrounds in planning behavioral experiments.

Clinical characterisation of patients in the post-acute stage of anti-NMDA receptor encephalitis: a prospective cohort study and comparison with patients with schizophrenia spectrum disorders.

BACKGROUND: Anti-NMDA receptor (NMDAR) encephalitis is associated with a post-acute stage that is not well known. We aimed to describe the clinical features of this stage, similarities with schizophrenia spectrum disorders, and the factors that predict cognitive-psychiatric outcomes and could serve as prognostic biomarkers. METHODS: In this prospective cohort study, participants (aged 12-60 years) with anti-NMDAR encephalitis during the post-acute stage visited Hospital Clínic de Barcelona (Barcelona, Spain) on three occasions (at study entry [V1], at 6 months [V2], and at 12 months [V3]) and underwent comprehensive neuropsychiatric evaluations. Similar evaluations were done in a group of age-matched participants with schizophrenia spectrum disorders and a group of age-matched and sex-matched healthy participants also recruited from Hospital Clínic de Barcelona. We analysed differences between and within groups in the longitudinal follow-up using multilevel linear mixed-effect models, adjusting for group, age, sex, and socioeconomic status to control for possible confounding. FINDINGS: Between Jan 1, 2017, and Sept 30, 2020, 82 participants were recruited, 28 (34%) with anti-NMDAR encephalitis, 27 (33%) with schizophrenia spectrum disorders, and 27 (33%) healthy participants. Although, by V1 (median 4 months [IQR 3-7] from disease onset), many acute-stage symptoms in participants with anti-NMDAR encephalitis had resolved (acute stage median modified Rankin Scale [mRS] score 5 [IQR 4-5] vs V1 mRS score 2 [1-2]; p<0·0001), 25 (89%) participants showed deficits in at least one cognitive domain. In this group, 15 (68%) of 22 cognitive domain variables were impaired at V1, whereas only eight (36%) were altered at V3 (p=0·016). In participants with schizophrenia spectrum disorders, 11 (50%) of 22 variables (all shared with participants with anti-NMDAR encephalitis) were impaired at V1, without changes at V3. Two acute-stage features of anti-NMDAR encephalitis (ie, decreased consciousness and no improvement within the first 4 weeks of treatment) predicted cognitive domain outcomes, and a visuospatial task (ie, serial biases) at V1 showed potential in predicting learning and memory outcomes. At V1, all psychiatric symptom clusters were similarly altered in participants with anti-NMDAR encephalitis and in those with schizophrenia spectrum disorders, but only those in individuals with anti-NMDAR encephalitis subsequently improved (p=0·031). The greatest cognitive-psychiatric improvement in participants with anti-NMDAR encephalitis occurred between V1 and V2. During this interval, four (14%) participants with anti-NMDAR encephalitis would have met the diagnostic criteria of schizophrenia if CSF antibody findings had not been investigated. INTERPRETATION: The cognitive-psychiatric symptoms of anti-NMDAR encephalitis in the post-acute stage resembled those of stabilised schizophrenia, but only those in participants with anti-NMDAR encephalitis progressively improved, predominantly during V1-V2. These findings are important for clinical trials on anti-NMDAR encephalitis and suggest that prompt cognitive-psychosocial rehabilitation might be a valuable intervention. FUNDING: Instituto Salud Carlos III, NEURON Network of European Funding for Neuroscience Research, National Alliance for Research in Schizophrenia and Affective Disorders, and la Caixa Health-Research Foundation.

Towards biologically constrained attractor models of schizophrenia.

Alterations in neuromodulation or synaptic transmission in biophysical attractor network models, as proposed by the dominant dopaminergic and glutamatergic theories of schizophrenia, successfully mimic working memory (WM) deficits in people with schizophrenia (PSZ). Yet, multiple, often opposing alterations in memory circuits can lead to the same behavioral patterns in these network models. Here, we critically revise the computational and experimental literature that links NMDAR hypofunction to WM precision loss in PSZ. We show in network simulations that currently available experimental evidence cannot set apart competing biophysical accounts. Critical points to resolve are the effects of increases vs. decreases in E/I ratio (e.g. through NMDAR blockade) on firing rate tuning and shared noise modulations and possible concomitant deficits in short-term plasticity. We argue that these concerted experimental and computational efforts will lead to a better understanding of the neurobiology underlying cognitive deficits in PSZ.

A mechanistically interpretable model of the retinal neural code for natural scenes with multiscale adaptive dynamics.

The visual system processes stimuli over a wide range of spatiotemporal scales, with individual neurons receiving input from tens of thousands of neurons whose dynamics range from milliseconds to tens of seconds. This poses a challenge to create models that both accurately capture visual computations and are mechanistically interpretable. Here we present a model of salamander retinal ganglion cell spiking responses recorded with a multielectrode array that captures natural scene responses and slow adaptive dynamics. The model consists of a three-layer convolutional neural network (CNN) modified to include local recurrent synaptic dynamics taken from a linear-nonlinear-kinetic (LNK) model [1]. We presented alternating natural scenes and uniform field white noise stimuli designed to engage slow contrast adaptation. To overcome difficulties fitting slow and fast dynamics together, we first optimized all fast spatiotemporal parameters, then separately optimized recurrent slow synaptic parameters. The resulting full model reproduces a wide range of retinal computations and is mechanistically interpretable, having internal units that correspond to retinal interneurons with biophysically modeled synapses. This model allows us to study the contribution of model units to any retinal computation, and examine how long-term adaptation changes the retinal neural code for natural scenes through selective adaptation of retinal pathways.

Reduced serial dependence suggests deficits in synaptic potentiation in anti-NMDAR encephalitis and schizophrenia.

A mechanistic understanding of core cognitive processes, such as working memory, is crucial to addressing psychiatric symptoms in brain disorders. We propose a combined psychophysical and biophysical account of two symptomatologically related diseases, both linked to hypofunctional NMDARs: schizophrenia and autoimmune anti-NMDAR encephalitis. We first quantified shared working memory alterations in a delayed-response task. In both patient groups, we report a markedly reduced influence of previous stimuli on working memory contents, despite preserved memory precision. We then simulated this finding with NMDAR-dependent synaptic alterations in a microcircuit model of prefrontal cortex. Changes in cortical excitation destabilized within-trial memory maintenance and could not account for disrupted serial dependence in working memory. Rather, a quantitative fit between data and simulations supports alterations of an NMDAR-dependent memory mechanism operating on longer timescales, such as short-term potentiation.

Interplay between persistent activity and activity-silent dynamics in the prefrontal cortex underlies serial biases in working memory.

Persistent neuronal spiking has long been considered the mechanism underlying working memory, but recent proposals argue for alternative 'activity-silent' substrates. Using monkey and human electrophysiology data, we show here that attractor dynamics that control neural spiking during mnemonic periods interact with activity-silent mechanisms in the prefrontal cortex (PFC). This interaction allows memory reactivations, which enhance serial biases in spatial working memory. Stimulus information was not decodable between trials, but remained present in activity-silent traces inferred from spiking synchrony in the PFC. Just before the new stimulus, this latent trace was reignited into activity that recapitulated the previous stimulus representation. Importantly, the reactivation strength correlated with the strength of serial biases in both monkeys and humans, as predicted by a computational model that integrates activity-based and activity-silent mechanisms. Finally, single-pulse transcranial magnetic stimulation applied to the human PFC between successive trials enhanced serial biases, thus demonstrating the causal role of prefrontal reactivations in determining working-memory behavior.

Responsiveness of patient-based and external rating scales in multiple sclerosis: head-to-head comparison in three clinical settings.

BACKGROUND: Patient-based rating scales and especially quality of life scales have received increasing attention as secondary outcome measures in multiple sclerosis (MS). Responsiveness to health-related change of quality of life scales is thus an important property when these measures are to be used successfully in clinical trials. METHODS: We conducted an analysis of 3 cohorts of MS patients to examine responsiveness of the Hamburg Quality of Life Questionnaire for Multiple Sclerosis (HAQUAMS). One cohort consisted of patients from the outpatient clinic whose overall health status deteriorated over the course of one year (n=53), one study investigated two neurorehabilitation programs (n=20 each) and a third study investigated a low-level aerobic fitness training intervention (n=15). RESULTS: The total score of the HAQUAMS and several subscales was found to be responsive in all three settings. In addition, we provide minimally important difference (MID) estimates based on anchor- and distribution-based methods for all scales of the HAQUAMS. CONCLUSIONS: The HAQUAMS is responsive to change in observational and intervention studies in MS in adequately powered trials.