Author: admin

  • Sleep amplifies epileptics’ dementia risk

    Sleep amplifies epileptics’ dementia risk

    Epilepsy is periodic overactivity in the brain, and is known to give rise to cognitive impairment. In a recent study, we examined data from 42,000 people, examining the risk of dementia (Tai et al. Neurology 2026). Although epilepsy was a risk factor for dementia, poor sleep multiplied this risk considerably — i.e. much more than…

  • Efficient memory for what we did

    Efficient memory for what we did

    Autobigraphical memory, our memory for events, may employ a code that economically uses existing knowledge. This helps us remove redundancies: we can store a bit less, because we can use background knowledge to reconstruct parts of the events. In this theoretical paper (Mazor et al, Neurosci Biobeh Rev 2026), we propose that our memory makes…

  • When we mix things up up, we don’t always recombine them

    When we mix things up up, we don’t always recombine them

    When we hold two things in memory, we might exchange a feature of one object with the other. For example, if I saw an apple on the table, and an orange on the shelf, I might swap them round. In this set of experiments (Tabi et al Sci Rep 2026), we developed a task that…

  • Pupils as a clue to Parkinson’s

    Pupils as a clue to Parkinson’s

    Autonomic function can be impaired long before the diagnosis of Parkinson’s disease. One way to easily measure autonomic function is through the pupillary reflexes. In this review (Dawidziuk et al, Diagnostics 2025) we critically appraised pupillometry in Parkinson’s disease. We highlight potential future avenues to integrate multidimensional quantitative pupillometry in clinic and research.

  • Brain stimulation reduces biases

    Brain stimulation reduces biases

    Deep brain stimulation can be used to treat a growing number of conditions. It also allows us a new way to test how the brain works. In a recent study (Hirschbichler et al. Brain 2026) we asked a rare group of patients with electrodes in the ventral tegmental area to learn choices from feedback. we…

  • Explaining ourselves by confabulation

    Explaining ourselves by confabulation

    Consciousness is sometimes described as the ineffable separation between the subjective and objective worlds. Battaglia et al (2025) proposed that this arises due to a Markov blanket – an informational boundary that gives us two incompatible views of our minds: a view from the inside, that can never fully align with the view of ourselves…

  • Many mechanisms of apathy in Parkinson’s disease

    Many mechanisms of apathy in Parkinson’s disease

    The brain degeneration in Parkinson’s disease can take away motivation – a condition called clinical apathy. Though apathy is one symptom, it has different components. It creates a feeling of effort, diminishes our sense of pleasure, reduces our ability to plan ahead, affects arousal, and makes us more ‘content’ to stay in the status quo.…

  • Patterns of cognitive change to diagnose atypical Parkinson’s?

    Patterns of cognitive change to diagnose atypical Parkinson’s?

    Diagnosing Parkinson’s disease is hard because of the many variants, which may have different underlying causes. Currently, arbitrary clinical criteria are used to label patitents. Harnessing two large cohorts of patients from across the UK (total N=1138), we found that patients with different labels also had different patterns on cognitive and psychiatric measures (Hu et…

  • Difficulty planning after brain damage

    Difficulty planning after brain damage

    Planning ahead requires several component processes. Imagine you are playing a game of connect four. You have to keep track of multiple steps in the future, notice all the possiblities, and evaluate how good each option is. We studied patients with damage to the medial prefrontal cortex, a brain area usually associated with evaluation (Holton…

  • A neural network to time movements

    A neural network to time movements

    Walking, chewing, scratching, hammering… Many movements require repeating at regular intervals – but can run at various speeds. How can a neural circuit flexibly generate these patterns? We solved the problem using active inference: a framework in which we predict the sensory consequences of upcoming actions, which entrains low-level reflexes to generate those appropriate actions…