News & Comment

Lesion network mapping (LNM), or atrophy network mapping, has become a widely adopted tool for linking focal brain lesions or neurodegenerative brain clusters, respectively, to distributed functional networks associated with cognitive or clinical deficits. Recent insights, however, suggest that LNM primarily captures elementary topological properties of the normative connectome rather than disorder-specific circuits. Independent clinical evidence supports these methodological concerns, reflecting a deeper biological issue. LNM is inherently unable to capture the higher-order disconnection effects and non-linear connectivity changes that characterize the brain response to a broad range of neurological conditions. Brain injuries can induce widespread changes in distal regions not directly affected by the damage, as well as complex patterns of pathological hyperconnectivity and hypoconnectivity that evolve over time and whose functional significance remains uncertain. These phenomena represent a central challenge in clinical neuroscience. LNM is intrinsically limited in capturing these dynamics, with important implications for clinical translation and neuromodulation.

Decades of male bias in animal research have left female biology critically understudied. Prematurely phasing out animal research would lock this inequity into future biomedicine, disproportionately harming women.

Van den Heuvel et al. show that lesion-network mapping (LNM)-derived circuits converge across disorders and argue that this convergence reflects a fundamental methodological limitation of LNM. We evaluate ways forward for future LNM studies, including robust null models, in light of these concerns. We view recapitulation of connectome modularity and high-order network properties by LNM as clinically informative and biologically meaningful.

As neuroscience increasingly recognizes that understanding the brain requires studying natural behavior, it has begun to adopt more naturalistic experimental environments as a means to that end — an important and welcome shift. Yet environmental realism alone does not guarantee that natural behavior is being studied and, in some cases, can create the illusion of ecological relevance or even promote unnatural behavior if the behavioral context is poorly aligned with a species’ ecology. Keeping sight of our central goal — understanding how brains support the actions animals evolved to perform — requires an ethological focus not only on where experiments occur, but on what animals are actually doing and whether the environment affords those behaviors.

Across the globe, human environments and experiences are diverse and undergoing rapid transformation. With the growing prevalence of neurological and mental health challenges, there is now an urgent imperative to understand the impacts of this diversity and change on the brain. This will require large-scale and long-term global studies of neural activity coupled with measures of lifestyle and life experience, environmental exposures, and mental and cognitive outcomes across diverse populations that lend themselves to untangling multivariate effects. We describe our experience developing large-scale EEG neuroimaging data acquisition programs in India and Tanzania and highlight key considerations for ensuring that such programs are ethically sound, cost-effective, scalable, adaptable and capable of producing high-quality data.