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1. Deep cerebellar stimulation reduces ataxic motor symptoms in the shaker rat

   https://doi.org/10.1002/ana.25464  

   Anderson, Collin J. ; Figueroa, Karla P. ; Dorval, Alan D. ; Pulst, Stefan M. ( April 2019 , Annals of Neurology)

   Degenerative cerebellar ataxias (DCAs) affect up to 1 in 5,000 people worldwide, leading to incoordination, tremor, and falls. Loss of Purkinje cells, nearly universal across DCAs, dysregulates the dentatothalamocortical network. To address the paucity of treatment strategies, we developed an electrical stimulation‐based therapy for DCAs targeting the dorsal dentate nucleus.

   We tested this therapeutic strategy in the Wistar Furthshakerrat model of Purkinje cell loss resulting in tremor and ataxia. We implantedshakerrats with stimulating electrodes targeted to the dorsal dentate nucleus and tested a spectrum of frequencies ranging from 4 to 180 Hz.

   Stimulation at 30 Hz most effectively reduced motor symptoms. Stimulation frequencies >100 Hz, commonly used for parkinsonism and essential tremor, worsened incoordination, and frequencies within the tremor physiologic range may worsen tremor.

   Low‐frequency deep cerebellar stimulation may provide a novel strategy for treating motor symptoms of degenerative cerebellar ataxias. Ann Neurol 2019;85:681–690

    

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2. Theory and model of thalamocortical processing in decision making under uncertainty. Thalamocortical Interactions Gordon Research Conference 2024, Ventura, California, February 2024. Abstract, Poster.

   Wang, Mien Brabeeba ; Lynch, Nancy ; Halassa, Michael ( February 2024 , Thalamocortical Interactions Gordon Research Conference 2024)

   Animals flexibly select actions that maximize future rewards despite facing uncertainty in sen- sory inputs, action-outcome associations or contexts. The computational and circuit mechanisms underlying this ability are poorly understood. A clue to such computations can be found in the neural systems involved in representing sensory features, sensorimotor-outcome associations and contexts. Specifically, the basal ganglia (BG) have been implicated in forming sensorimotor-outcome association [1] while the thalamocortical loop between the prefrontal cortex (PFC) and mediodorsal thalamus (MD) has been shown to engage in contextual representations [2, 3]. Interestingly, both human and non-human animal experiments indicate that the MD represents different forms of uncertainty [3, 4]. However, finding evidence for uncertainty representation gives little insight into how it is utilized to drive behavior. Normative theories have excelled at providing such computational insights. For example, de- ploying traditional machine learning algorithms to fit human decision-making behavior has clarified how associative uncertainty alters exploratory behavior [5, 6]. However, despite their computa- tional insight and ability to fit behaviors, normative models cannot be directly related to neural mechanisms. Therefore, a critical gap exists between what we know about the neural representa- tion of uncertainty on one end and the computational functions uncertainty serves in cognition. This gap can be filled with mechanistic neural models that can approximate normative models as well as generate experimentally observed neural representations. In this work, we build a mechanistic cortico-thalamo-BG loop network model that directly fills this gap. The model includes computationally-relevant mechanistic details of both BG and thalamocortical circuits such as distributional activities of dopamine [7] and thalamocortical pro- jection modulating cortical effective connectivity [3] and plasticity [8] via interneurons. We show that our network can more efficiently and flexibly explore various environments compared to com- monly used machine learning algorithms and we show that the mechanistic features we include are crucial for handling different types of uncertainty in decision-making. Furthermore, through derivation and mathematical proofs, we approximate our models to two novel normative theories. We show mathematically the first has near-optimal performance on bandit tasks. The second is a generalization on the well-known CUMSUM algorithm, which is known to be optimal on single change point detection tasks [9]. Our normative model expands on this by detecting multiple sequential contextual changes. To our knowledge, our work is the first to link computational in- sights, normative models and neural realization together in decision-making under various forms of uncertainty. 

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3. Changes in Resting State Functional Connectivity Associated with Dynamic Adaptation of Wrist Movements

   https://doi.org/10.1523/JNEUROSCI.1916-22.2023  

   Farrens, Andria J. ; Vahdat, Shahabeddin ; Sergi, Fabrizio ( March 2023 , The Journal of Neuroscience)

   Dynamic adaptation is an error-driven process of adjusting planned motor actions to changes in task dynamics (Shadmehr, 2017). Adapted motor plans are consolidated into memories that contribute to better performance on re-exposure. Consolidation begins within 15 min following training (Criscimagna-Hemminger and Shadmehr, 2008), and can be measured via changes in resting state functional connectivity (rsFC). For dynamic adaptation, rsFC has not been quantified on this timescale, nor has its relationship to adaptative behavior been established. We used a functional magnetic resonance imaging (fMRI)-compatible robot, the MR-SoftWrist (Erwin et al., 2017), to quantify rsFC specific to dynamic adaptation of wrist movements and subsequent memory formation in a mixed-sex cohort of human participants. We acquired fMRI during a motor execution and a dynamic adaptation task to localize brain networks of interest, and quantified rsFC within these networks in three 10-min windows occurring immediately before and after each task. The next day, we assessed behavioral retention. We used a mixed model of rsFC measured in each time window to identify changes in rsFC with task performance, and linear regression to identify the relationship between rsFC and behavior. Following the dynamic adaptation task, rsFC increased within the cortico-cerebellar network and decreased interhemispherically within the cortical sensorimotor network. Increases within the cortico-cerebellar network were specific to dynamic adaptation, as they were associated with behavioral measures of adaptation and retention, indicating that this network has a functional role in consolidation. Instead, decreases in rsFC within the cortical sensorimotor network were associated with motor control processes independent from adaptation and retention.

   SIGNIFICANCE STATEMENTMotor memory consolidation processes have been studied via functional magnetic resonance imaging (fMRI) by analyzing changes in resting state functional connectivity (rsFC) occurring more than 30 min after adaptation. However, it is unknown whether consolidation processes are detectable immediately (<15 min) following dynamic adaptation. We used an fMRI-compatible wrist robot to localize brain regions involved in dynamic adaptation in the cortico-thalamic-cerebellar (CTC) and cortical sensorimotor networks and quantified changes in rsFC within each network immediately after adaptation. Different patterns of change in rsFC were observed compared with studies conducted at longer latencies. Increases in rsFC in the cortico-cerebellar network were specific to adaptation and retention, while interhemispheric decreases in the cortical sensorimotor network were associated with alternate motor control processes but not with memory formation.

    

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4. Thalamostriatal and cerebellothalamic pathways in a songbird, the Bengalese finch

   https://doi.org/10.1002/cne.24428  

   Nicholson, David A. ; Roberts, Todd F. ; Sober, Samuel J. ( April 2018 , Journal of Comparative Neurology)

   The thalamostriatal system is a major network in the mammalian brain, originating principally from the intralaminar nuclei of thalamus. Its functions remain unclear, but a subset of these projections provides a pathway through which the cerebellum communicates with the basal ganglia. Both the cerebellum and basal ganglia play crucial roles in motor control. Although songbirds have yielded key insights into the neural basis of vocal learning, it is unknown whether a thalamostriatal system exists in the songbird brain. Thalamic nucleus DLM is an important part of the song system, the network of nuclei required for learning and producing song. DLM receives output from song system basal ganglia nucleus Area X and sits within dorsal thalamus, the proposed avian homolog of the mammalian intralaminar nuclei that also receives projections from the cerebellar nuclei. Using a viral vector that specifically labels presynaptic axon segments, we show in Bengalese finches that dorsal thalamus projects to Area X, the basal ganglia nucleus of the song system, and to surrounding medial striatum. To identify the sources of thalamic input to Area X, we map DLM and cerebellar‐recipient dorsal thalamus (DT_CbN). Surprisingly, we find both DLM and dorsal anterior DT_CbNadjacent to DLM project to Area X. In contrast, the ventral medial subregion of DT_CbNprojects to medial striatum outside Area X. Our results suggest the basal ganglia in the song system, like the mammalian basal ganglia, integrate feedback from the thalamic region to which they project as well as thalamic regions that receive cerebellar output.

    

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5. Disentangling disorders of consciousness: Insights from diffusion tensor imaging and machine learning

   https://doi.org/10.1002/hbm.23370  

   Zheng, Zhong S. ; Reggente, Nicco ; Lutkenhoff, Evan ; Owen, Adrian M. ; Monti, Martin M. ( September 2016 , Human Brain Mapping)

   Previous studies have suggested that disorders of consciousness (DOC) after severe brain injury may result from disconnections of the thalamo‐cortical system. However, thalamo‐cortical connectivity differences between vegetative state (VS), minimally conscious state minus (MCS−, i.e., low‐level behavior such as visual pursuit), and minimally conscious state plus (MCS+, i.e., high‐level behavior such as language processing) remain unclear. Probabilistic tractography in a sample of 25 DOC patients was employed to assess whether structural connectivity in various thalamo‐cortical circuits could differentiate between VS, MCS−, and MCS+ patients. First, the thalamus was individually segmented into seven clusters based on patterns of cortical connectivity and tested for univariate differences across groups. Second, reconstructed whole‐brain thalamic tracks were used as features in a multivariate searchlight analysis to identify regions along the tracks that were most informative in distinguishing among groups. At the univariate level, it was found that VS patients displayed reduced connectivity in most thalamo‐cortical circuits of interest, including frontal, temporal, and sensorimotor connections, as compared with MCS+, but showed more pulvinar‐occipital connections when compared with MCS−. Moreover, MCS− exhibited significantly less thalamo‐premotor and thalamo‐temporal connectivity than MCS+. At the multivariate level, it was found that thalamic tracks reaching frontal, parietal, and sensorimotor regions, could discriminate, up to 100% accuracy, across each pairwise group comparison. Together, these findings highlight the role of thalamo‐cortical connections in patients' behavioral profile and level of consciousness. Diffusion tensor imaging combined with machine learning algorithms could thus potentially facilitate diagnostic distinctions in DOC and shed light on the neural correlates of consciousness.Hum Brain Mapp 38:431–443, 2017. ©2016 Wiley Periodicals, Inc.

    

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