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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.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.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]; p0·0001), 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.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.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.

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. The authors are supported by the Spanish Ministry of Economy and Competitiveness (RYC-2017-23231 to A.H.), the “la Caixa” Banking Foundation (Ref: LCF/BQ/IN17/11620008, H.S.), the European Union’s Horizon 2020 Marie Skłodowska-Curie grant (Ref: 713673, H.S.), and the European Molecular Biology Organization (Ref: EMBO ALTF 471-2021, H.S.). JB was supported by the Fyssen Foundation and by the Bial Foundation (Ref: 356/18). S.S-F. is funded by Ministerio de Ciencia e Innovación (Ref: PID2019-108531GB-I00 AEI/FEDER), AGAUR Generalitat de Catalunya (Ref: 2017 SGR 1545), and the FEDER/ERFD Operative Programme for Catalunya 2014-2020.

Recent work has argued that large-scale neural recordings are often well described by low-dimensional ‘latent’ dynamics identified using dimensionality reduction. However, the view that task-relevant variability is shared across neurons misses other types of structure underlying behavior, including stereotyped neural sequences or slowly evolving latent spaces. To address this, we introduce a new framework that simultaneously accounts for variability that is shared across neurons, trials, or time. To identify and demix these covariability classes, we develop a new unsupervised dimensionality reduction method for neural data tensors called sliceTCA. In three example datasets, including motor cortical dynamics during a classic reaching task and recent multi-region recordings from the International Brain Laboratory, 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.

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.

The cerebellum has long been regarded as synonymous with motor function, while cognitive neuroscientists have often ignored this brain area. However, over the last 30 years, clinical observations ([Schmahmann et al., 2019][1]) and human neuroimaging ([Diedrichsen et al., 2019][2]) have identified

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.

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., Stein, Barbosa et al. show that anti-NMDAR encephalitis and schizophrenia are characterized by reduced serial dependence in spatial working memory. Cortical network simulations show that this can be parsimoniously explained by a reduction in NMDAR-dependent short-term synaptic potentiation in these diseases.

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.

We report markedly reduced working memory-related serial dependence with preserved memory accuracy in anti-NMDAR encephalitis and schizophrenia. We argue that NMDAR-related changes in cortical excitation, while quickly destabilizing persistent neural activity, cannot fully account for a reduction of memory-dependent biases. Rather, our modeling results support a disruption of a memory mechanism operating on a longer timescale, such as short-term potentiation.

Persistent neuronal spiking has long been considered the mechanism underlying working memory, but recent proposals argue for alternative, “activity-silent” substrates for memory. Using monkey and human electrophysiology, we show here that attractor dynamics that control neural spiking during mnemonic periods interact with activity-silent mechanisms in PFC. This interaction allows memory reactivation, 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 PFC. Just prior to 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 integrating activity-based and activity-silent mechanisms. Finally, single-pulse TMS applied to human prefrontal cortex prior to trial start enhanced serial biases, demonstrating the causal role of prefrontal reactivations in determining working memory behavior.

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.

ZusammenfassungDie Therapie mit Antikoagulanzien und Thrombolytika ist unter strenger Indikationsstellung ein bewährtes Behandlungskonzept. Die häufigste Komplikation ist die Blutung. Dabei gehören intrakranielle Blutungen zu den seltenen, jedoch aus prognostischer Sicht zu den ernsthafteren, vital bedrohlichen Komplikationen. Unter 283 Patienten mit einem spontanen intrazerebralen Hämatom fanden sich 42 Patienten (14,8%), bei denen die Blutung unter einer gerinnungshemmenden Therapie aufgetreten war. Bei 24 Patienten (8,5%) war es unter einer Phenpro-coumon-Behandlung, bei 18 Patienten (6,3%) nach einer Thrombolysetherapie zur Blutung gekommen. Risikofaktoren wie Alter über 70 Jahre (n = 15), Hyper-tonus (n = 25), Diabetes mellitus (n = 3), Medikamenteninteraktionen (n = 11), Zustand nach stattgehabter intrazerebraler Blutung (n = 3) wurden analysiert. Insgesamt 26 Patienten wurden operativ, 16 Patienten konservativ behandelt. Die zerebral bedingte Letalität betrug 42,8%, in der Marcumar®-Gruppe 41,6%, in der Thrombolyse-Gruppe 44,4%. Die Indikation zur oralen Antikoagulation sollte sorgfältig gestellt und im Laufe der Behandlung wiederholt überprüft werden, um die Behandlungsdauer auf den absolut erforderlichen Zeitraum zu beschränken. Der sorgfältigen Beachtung von Medikamenteninteraktionen, akuter interkurrenter Erkrankungen sowie der Dynamik der mit zunehmendem Alter häufig vergesellschafteten Multimorbidität kommt besondere Bedeutung zu.

Although it is now 60 years after Erdheim's (1931) detailed description of vertebral alterations in severe acromegaly, it is still unclear whether osteoporosis is a consistent feature of acromegalic bone disease or not. We studied the vertebral trabecular bone of a 44-year-old woman who had suffered active acromegaly for more than 20 years, and compared it with 17 normal as well as 2 osteoporotic controls. Histomorphometry revealed a very low trabecular bone volume and thus documented the presence of osteoporosis. The mean trabecular plate thickness was strikingly increased in acromegaly (possibly caused in part by a low-dose fluoride treatment), whereas it was normal or reduced in the osteoporotic controls. The meticulous analysis showed islands of cartilaginous tissue in the core of the acromegalic trabeculae which were not present in any other sample. In these areas collagen II was detected by immunohistochemistry. Biochemical analysis revealed that collagen II accounted for 7% of the total collagenous matrix. The degree of hydroxylation of lysyl residues of collagen I was close to the average value of all control samples studied. Our data show that osteoporosis can occur in acromegaly and that it is characterized by unusual architectural and compositional features. These findings challenge the prevailing view that the matrix of osteoporotic bone always shows a normal composition.