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Primary progressive aphasia (PPA) is a clinical neurodegenerative syndrome with word finding problems as a core clinical symptom. Many aspects of word finding have been clarified in psycholinguistics using picture naming and a picture-word interference (PWI) paradigm, which emulates naming under contextual noise. However, little is known about how word finding depends on white-matter tract integrity, in particular, the atrophy of tracts located ventrally to the Sylvian fissure. To elucidate this question, we examined word finding in individuals with PPA and healthy controls employing PWI, tractography, and computer simulations using the WEAVER++ model of word finding. Twenty-three individuals with PPA and twenty healthy controls named pictures in two noise conditions. Mixed-effects modelling was performed on naming accuracy and reaction time (RT) and fixel-based tractography analyses were conducted to assess the relation between ventral white-matter integrity and naming performance. Naming RTs were longer for individuals with PPA compared to controls and, critically, individuals with PPA showed a larger noise effect compared to controls. Moreover, this difference in noise effect was differentially related to tract integrity. Whereas the noise effect did not depend much on tract integrity in controls, a lower tract integrity was related to a smaller noise effect in individuals with PPA. Computer simulations supported an explanation of this paradoxical finding in terms of reduced propagation of noise when tract integrity is low. By using multimodal analyses, our study indicates the significance of the ventral pathway for naming and the importance of RT measurement in the clinical assessment of PPA.

Background: Apathy is the neuropsychiatric syndrome that correlates most highly with Huntington's disease progression, and, like early patterns of neurodegeneration, is associated with lesions to cortico-striatal connections. However, due to its multidimensional nature and elusive etiology, treatment options are limited. Objectives: To disentangle underlying white matter microstructural correlates across the apathy spectrum in Huntington's disease. Methods: Forty-six Huntington's disease individuals (premanifest (N = 22) and manifest (N = 24)) and 35 healthy controls were scanned at 3-tesla and underwent apathy evaluation using the short-Problem Behavior Assessment and short-Lille Apathy Rating Scale, with the latter being characterized into three apathy domains, namely emotional, cognitive, and auto-activation deficit. Diffusion tensor imaging was used to study whether individual differences in specific cortico-striatal tracts predicted global apathy and its subdomains. Results: We elucidate that apathy profiles may develop along differential timelines, with the auto-activation deficit domain manifesting prior to motor onset. Furthermore, diffusion tensor imaging revealed that inter-individual variability in the disruption of discrete cortico-striatal tracts might explain the heterogeneous severity of apathy profiles. Specifically, higher levels of auto-activation deficit symptoms significantly correlated with increased mean diffusivity in the right uncinate fasciculus. Conversely, those with severe cognitive apathy demonstrated increased mean diffusivity in the right frontostriatal tract and left dorsolateral prefrontal cortex to caudate nucleus tract. Conclusions: The current study provides evidence that white matter correlates associated with emotional, cognitive, and auto-activation subtypes may elucidate the heterogeneous nature of apathy in Huntington's disease, as such opening a door for individualized pharmacological management of apathy as a multidimensional syndrome in other neurodegenerative disorders. Keywords: Apathy, Diffusion MRI, Huntington's disease, Individual differences, Neurodegeneration, White matter microstructure

In the present study, we aimed to test the association between the correct function of the left ventral white matter pathways and semantic processing (dual stream models for language processing, Hickok & Poeppel, 2004), using a new set of language tasks during intraoperative electrical stimulation at white matter level. Additionally, we evaluated brain regions needed for correct performance on the different semantic tasks using lesion-symptom analyses (voxel lesion-symptom mapping and track-wise lesion analysis) in a sample of 62 candidates for the awake brain surgery. We found that electrical stimulation in the vicinity of the inferior longitudinal and inferior fronto-occipital fasciculi disturbed performance on semantic processing tasks. Individuals presented with significantly more semantic paraphasias during brain tumor resection than during the electrical stimulation at the cortex level. Track-wise analyses confirmed the role of these left ventral pathways in semantic processing: a significant relationship was observed between the probability of inferior fronto-occipital fasciculus disconnection/damage and the semantic matching tasks, as well as the number of semantic paraphasias in naming. Importantly, the same analyses for the total score of the Boston Naming Test confirmed significant relationships between this test score and the integrity of the inferior fronto-occipital, inferior longitudinal and uncinate fasciculi. This was further supported by the results of VLSM analyses showing a significant relationship between BNT and the presence of lesion within left middle and inferior temporal gyri. The present findings provide new intraoperative evidence for the role of the white-matter ventral pathways in semantic processing, while at the same time emphasizing the need to include a broader assessment of semantic-conceptual aspects during the awake neurosurgical intervention. This approach will ensure better preservation of functional tissue in the tumoral vicinity and therefore substantially diminish post-surgical language impairments. Keywords: ESM = electrical stimulation mapping, Dual stream models for language processing, Diffusion tensor imaging (DTI), Semantic processing, Voxel lesion-symptom mapping (VLSM)