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Remediating Brain Instabilities in a Neurology Practice
Published in Hanno W. Kirk, Restoring the Brain, 2020
When the training protocol and optimal reinforcement frequency (ORF) are ideal, the EEG-based neuromodulation and infra-low frequency brain training methods promote a powerful re-regulation of the patient’s brain networks, compelling them toward a recovery (or rediscovery or re-learning) of its inherent stability. This occurs after any productive session and may sustain only over the next day or two, thus requiring practice and repetition over time, as when learning a skill (like learning to play the guitar or to play golf), to eventuate toward a global brain homeostasis “reset” scenario wherein a new competence is incorporated in the system. Synaptic adaptation (Hebbian learning) and homeostatic plasticity mechanisms are implicated.6,7,8,9 The responsive results in the above vignettes serve to demonstrate the capacity for neuroplasticity at any age. Children self-report (or their parents report) immediate benefits that sustain more readily; the “re-learning” in children appears precipitous. At any age, the more severely dysregulated the central nervous system (CNS) has become, the more immediately the desired anticipated response becomes observable, as the above cases for patients A and B illustrate in elderly adults.
The past, present, and future of errorless learning in memory rehabilitation
Published in Catherine Haslam, Roy P.C. Kessels, Errorless Learning in Neuropsychological Rehabilitation, 2018
Barbara A. Wilson, Jessica E. Fish
Another way of understanding EL is to apply principles of Hebbian plasticity and learning (Hebb, 1949). At a synaptic level, Hebbian plasticity refers to increases in synaptic strength between neurons that fire together (“neurons that fire together wire together”). Hebbian learning refers to the detection of temporally correlated inputs. If an input elicits a pattern of neural activity, then, according to the Hebbian Learning rule, the tendency to activate the same pattern on subsequent occasions is strengthened. This means that the likelihood of making the same response in the future, whether correct or incorrect, is strengthened (McClelland, Thomas, McCandliss, & Fiez, 1999). Like implicit memory, Hebbian learning has no mechanism for filtering out errors.
Mechanisms of Recovery After Acquired Brain Injury
Published in Barbara A. Wilson, Jill Winegardner, Caroline M. van Heugten, Tamara Ownsworth, Neuropsychological Rehabilitation, 2017
Hebbian learning and neural reconnection have been repeatedly proposed as mechanisms that might explain rehabilitation outcomes, by coupling learning on a behavioural level with changes on the physiological level, in particular the increase in synaptic strength between neurons that fire together as a result of learning experiences (Hillis, 2005; Robertson and Murre, 1999). However, empirical support for this explanation is still lacking, most probably due to the same reasons that diaschisis has been a lingering concept for many years: the absence of sophisticated imaging methods that allow verification of the principles of Hebbian learning. Progress in medical imaging, especially in the fine-grained mapping of brain connectivity patterns, might foster empirical support of this theory of recovery in the near future.
Effects of music production on cortical plasticity within cognitive rehabilitation of patients with mild traumatic brain injury
Published in Brain Injury, 2018
Berit Marie Dykesteen Vik, Geir Olve Skeie, Eirik Vikane, Karsten Specht
Although speculative, a possible explanation for the reorganisation of neural networks during musical training may be the factor of shared neural networks for language/music (30–33). Association is a key word in explaining how new neural pathways may be facilitated by interaction of neural networks between musical cognition and non-musical cognitive functions as brain areas serving cognition and emotion are overlapping and interconnected. Additional information from the literature within cognitive rehabilitation provides evidence that music stimulates cognition and perception in activating neural networks engaged in attention by analysing perceptual patterns in music. Fundamental organisational processes for memory formation in music have their parallels in temporal chunking principles in non-musical memory processes (semantic and episodic memory) (34). Together with Hebbian learning rules, whereby synapses are driven to change by coherent inputs in a competitive neural network (9), these four interconnected factors may be the major basic elements in how music supported interventions may reorganise a broken brain system.
Repetitive facilitative exercise under continuous electrical stimulation for recovery of pure motor isolated hand palsy after infarction of the “hand knob” area: A case report
Published in Physiotherapy Theory and Practice, 2023
Takashi Hoei, Kazumi Kawahira, Megumi Shimodozono, Hidefumi Fukuda, Keizo Shigenobu, Tadashi Ogura, Shuji Matsumoto
These favorable outcomes may be attributable to the high number of individual finger active-movement repetitions (2000 repetitions during an 80 minute session) used during RFE-under-cNMES. This hypothesis is based on the Hebbian learning theory (Subramanian, Massie, Malcolm, and Levin, 2010). Additionally, NMES has central or peripheral modulation effects (Bao, Khan, Song, and Tong, 2020). It has been reported that motor cortical excitability increases by combining active movement and NMES (Khaslavskaia and Sinkjaer, 2005). Thus, it is possible that hand function was improved by RFE-under-cNMES.
An exploration of aphasia therapy dosage in the first six months of stroke recovery
Published in Neuropsychological Rehabilitation, 2021
Emily Brogan, Natalie Ciccone, Erin Godecke
Learning within neurorehabilitation sessions is concerned both with client acts and inputs from the therapist (Kleim & Jones, 2008). The dosage at which these are delivered is crucial. Treatment sessions aim to capitalize on and enhance the brain’s natural inherent plasticity that underpins learning (Crosson et al., 2019). Neurorehabilitation focuses largely on practice dependent learning that occurs when an individual repeatedly uses a skill to induce lasting neuronal change (Robbins et al., 2008). This focus is grounded largely in Hebbian learning theory which proposes learning occurs by connections that develop through inputs that co-occur and can be summarized by “neurons that fire together wire together” (Hebb, 1949). It requires repetition to induce lasting cellular changes that add stability to the network (Pulvermuller & Berthier, 2008). Neuronal assemblies must be repeatedly active at the same time when forming links (Pulvermuller & Berthier, 2008). Increased repetition of a newly learned behaviour may be required to induce lasting neural changes (Kleim & Jones, 2008) and so, in theory, the more frequently two relevant brain events occur together, the more the critical connections will be strengthened (Pulvermuller & Berthier, 2008). Assuming that therapy can induce coincidental learning, it has been hypothesized that more training will help improve learning and may change behaviour (Pulvermuller & Berthier, 2008). It is also hypothesized that there is a point of diminishing return to increasing the amount of therapy and so the dosage associated with a therapeutic “sweet spot” is yet to be discovered (Yoder et al., 2012). Additionally, there is the term “reaction range” indicating that the response to therapy varies depending on the individuals brain functioning (Gottesman, 1963; Yoder et al., 2012). Both of these concepts need to be considered in neurorehabilitation as increasingly, particularly with aphasia therapy, intense may not be better (Godecke et al., 2018).