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Background: Online technology could potentially revolutionise how patients are cognitively assessed and monitored. However, it remains unclear whether assessments conducted remotely can match established pen-and-paper neuropsychological tests in terms of sensitivity and specificity. Methods: This observational study aimed to optimise an online cognitive assessment for use in traumatic brain injury (TBI) clinics. The tertiary referral clinic in which this tool has been clinically implemented typically sees patients a minimum of 6 months post-injury in the chronic phase. Between March and August 2019, we conducted a cross-group, cross-device and factor analyses at the St. Mary’s Hospital TBI clinic and major trauma wards at Imperial College NHS trust and St. George’s Hospital in London (UK), to identify a battery of tasks that assess aspects of cognition affected by TBI. Between September 2019 and February 2020, we evaluated the online battery against standard face-to-face neuropsychological tests at the Imperial College London research centre. Canonical Correlation Analysis (CCA) determined the shared variance between the online battery and standard neuropsychological tests. Finally, between October 2020 and December 2021, the tests were integrated into a framework that automatically generates a results report where patients’ performance is compared to a large normative dataset. We piloted this as a practical tool to be used under supervised and unsupervised conditions at the St. Mary’s Hospital TBI clinic in London (UK). Findings: The online assessment discriminated processing-speed, visual-attention, working-memory, and executive-function deficits in TBI. CCA identified two significant modes indicating shared variance with standard neuropsychological tests (r = 0.86, p < 0.001 and r = 0.81, p = 0.02). Sensitivity to cognitive deficits after TBI was evident in the TBI clinic setting under supervised and unsupervised conditions (F (15,555) = 3.99; p < 0.001). Interpretation: Online cognitive assessment of TBI patients is feasible, sensitive, and efficient. When combined with normative sociodemographic models and autogenerated reports, it has the potential to transform cognitive assessment in the healthcare setting. Funding: This work was funded by a National Institute for Health Research (NIHR) Invention for Innovation (i4i) grant awarded to DJS and AH (II-LB-0715-20006).

Introduction Traumatic brain injury (TBI) is a dementia risk factor, with Alzheimer's disease (AD) more common following injury. Patterns of neurodegeneration produced by TBI can be compared to AD and aging using volumetric MRI. Methods A total of 55 patients after moderate to severe TBI (median age 40), 45 with AD (median age 69), and 61 healthy volunteers underwent magnetic resonance imaging over 2 years. Atrophy patterns were compared. Results AD patients had markedly lower baseline volumes. TBI was associated with increased white matter (WM) atrophy, particularly involving corticospinal tracts and callosum, whereas AD rates were increased across white and gray matter (GM). Subcortical WM loss was shared in AD/TBI, but deep WM atrophy was TBI-specific and cortical atrophy AD-specific. Post-TBI atrophy patterns were distinct from aging, which resembled AD. Discussion Post-traumatic neurodegeneration 1.9–4.0 years (median) following moderate-severe TBI is distinct from aging/AD, predominantly involving central WM. This likely reflects distributions of axonal injury, a neurodegeneration trigger. Highlights We compared patterns of brain atrophy longitudinally after moderate to severe TBI in late-onset AD and healthy aging. Patients after TBI had abnormal brain atrophy involving the corpus callosum and other WM tracts, including corticospinal tracts, in a pattern that was specific and distinct from AD and aging. This pattern is reminiscent of axonal injury following TBI, and atrophy rates were predicted by the extent of axonal injury on diffusion tensor imaging, supporting a relationship between early axonal damage and chronic neurodegeneration.

Background and Purpose— To describe the epidemiology and associations of poststroke epilepsy (PSE) because there is limited evidence to inform clinicians and guide future research. Methods— Data were collected from the population-based South London Stroke Register of first strokes in a multiethnic inner-city population with a maximum follow-up of 12 years. Self-completed forms and interviews notified study organizers of epilepsy diagnosis. Kaplan–Meier methods and Cox models were used to assess associations with sociodemographic factors, clinical features, stroke subtype, and severity markers. Results— Three thousand three-hundred ten patients with no history of epilepsy presented with first stroke between 1995 and 2007, with a mean follow-up of 3.8 years. Two-hundred thirteen subjects (6.4%) had development of PSE. PSE incidence at 3 months and 1, 5, and 10 years were estimated at 1.5%, 3.5%, 9.0%, and 12.4%, respectively. Sex, ethnicity, and socioeconomic status were not associations, but markers of cortical location, including dysphasia, visual neglect, and field defect, along with stroke severity indices at presentation, including low Glasgow Coma Scale, incontinence, or poor function on Barthel Index, were associated with PSE on univariate analysis. Young age was independently associated with PSE, affecting 10.7% of patients aged 85 years ( P ≤0.001) on 10-year estimates. Independent predictors of PSE also included visual neglect, dysphasia, and stroke subtype, particularly total anterior circulation infarcts. Dysarthria was associated with reduced incidence. Conclusions— PSE is common, with risk continuing to increase outside the acute phase. Young age, cortical location, larger lesions, and hemorrhagic lesions are independent predictors.