There is more to brain connections than the mere transfer of signals between brain regions. Behavior and cognition emerge through cortical area interaction. This requires integration between local and distant areas orchestrated by densely connected networks. Brain connections determine the brain’s functional organization. The imaging of connections in the living brain has provided an opportunity to identify the driving factors behind the neurobiology of cognition. Connectivity differences between species and among humans have furthered the understanding of brain evolution and of diverging cognitive profiles. Brain pathologies amplify this variability through disconnections and, consequently, the disintegration of cognitive functions. The prediction of long-term symptoms is now preferentially based on brain disconnections. This paradigm shift will reshape our brain maps and challenge current brain models.

Stroke-induced aphasia is associated with adverse effects on quality of life and the ability to return to work. For patients and clinicians the possibility of relying on valid predictors of recovery is an important asset in the clinical management of stroke-related impairment. Age, level of education, type and severity of initial symptoms are established predictors of recovery. However, anatomical predictors are still poorly understood. In this prospective longitudinal study, we intended to assess anatomical predictors of recovery derived from diffusion tractography of the perisylvian language networks. Our study focused on the arcuate fasciculus, a language pathway composed of three segments connecting Wernicke’s to Broca’s region (i.e. long segment), Wernicke’s to Geschwind’s region (i.e. posterior segment) and Broca’s to Geschwind’s region (i.e. anterior segment). In our study we were particularly interested in understanding how lateralization of the arcuate fasciculus impacts on severity of symptoms and their recovery. Sixteen patients (10 males; mean age 60 ± 17 years, range 28–87 years) underwent post stroke language assessment with the Revised Western Aphasia Battery and neuroimaging scanning within a fortnight from symptoms onset. Language assessment was repeated at 6 months. Backward elimination analysis identified a subset of predictor variables (age, sex, lesion size) to be introduced to further regression analyses. A hierarchical regression was conducted with the longitudinal aphasia severity as the dependent variable. The first model included the subset of variables as previously defined. The second model additionally introduced the left and right arcuate fasciculus (separate analysis for each segment). Lesion size was identified as the only independent predictor of longitudinal aphasia severity in the left hemisphere [beta = −0.630, t (−3.129), P = 0.011]. For the right hemisphere, age [beta = −0.678, t (–3.087), P = 0.010] and volume of the long segment of the arcuate fasciculus [beta = 0.730, t (2.732), P = 0.020] were predictors of longitudinal aphasia severity. Adding the volume of the right long segment to the first-level model increased the overall predictive power of the model from 28% to 57% [ F (1,11) = 7.46, P = 0.02]. These findings suggest that different predictors of recovery are at play in the left and right hemisphere. The right hemisphere language network seems to be important in aphasia recovery after left hemispheric stroke. * Abbreviations : AICc : corrected Akaike Information Criterion WAB-R : Western Aphasia Battery Revised

Inter-individual differences can inform treatment procedures and—if accounted for—have the potential to significantly improve patient outcomes. However, when studying brain anatomy, these inter-individual variations are commonly unaccounted for, despite reports of differences in gross anatomical features, cross-sectional, and connectional anatomy. Brain connections are essential to facilitate functional organization and, when severed, cause impairments or complete loss of function. Hence, the study of cerebral white matter may be an ideal compromise to capture inter-individual variability in structure and function. We reviewed the wealth of studies that associate cognitive functions and clinical symptoms with individual tracts using diffusion tractography. Our systematic review indicates that tractography has proven to be a sensitive method in neurology, psychiatry, and healthy populations to identify variability and its functional correlates. However, the literature may be biased, as the most commonly studied tracts are not necessarily those with the highest sensitivity to cognitive functions and pathologies. Additionally, the hemisphere of the studied tract is often unreported, thus neglecting functional laterality and asymmetries. Finally, we demonstrate that tracts, as we define them, are not correlated with one, but multiple cognitive domains or pathologies. While our systematic review identified some methodological caveats, it also suggests that tract–function correlations might still be a promising tool in identifying biomarkers for precision medicine. They can characterize variations in brain anatomy, differences in functional organization, and predicts resilience and recovery in patients.