Non-fluent aphasia implies a relatively straightforward neurological condition characterized by limited speech output. However, it is an umbrella term for different underlying impairments affecting speech production. Several studies have sought the critical lesion location that gives rise to non-fluent aphasia. The results have been mixed but typically implicate anterior cortical regions such as Broca's area, the left anterior insula, and deep white matter regions. To provide a clearer picture of cortical damage in non-fluent aphasia, the current study examined brain damage that negatively influences speech fluency in patients with aphasia. It controlled for some basic speech and language comprehension factors in order to better isolate the contribution of different mechanisms to fluency, or its lack. Cortical damage was related to overall speech fluency, as estimated by clinical judgements using the Western Aphasia Battery speech fluency scale, diadochokinetic rate, rudimentary auditory language comprehension, and executive functioning (scores on a matrix reasoning test) in 64 patients with chronic left hemisphere stroke. A region of interest analysis that included brain regions typically implicated in speech and language processing revealed that non-fluency in aphasia is primarily predicted by damage to the anterior segment of the left arcuate fasciculus. An improved prediction model also included the left uncinate fasciculus, a white matter tract connecting the middle and anterior temporal lobe with frontal lobe regions, including the pars triangularis. Models that controlled for diadochokinetic rate, picture-word recognition, or executive functioning also revealed a strong relationship between anterior segment involvement and speech fluency. Whole brain analyses corroborated the findings from the region of interest analyses. An additional exploratory analysis revealed that involvement of the uncinate fasciculus adjudicated between Broca's and global aphasia, the two most common kinds of non-fluent aphasia. In summary, the current results suggest that the anterior segment of the left arcuate fasciculus, a white matter tract that lies deep to posterior portions of Broca's area and the sensory-motor cortex, is a robust predictor of impaired speech fluency in aphasic patients, even when motor speech, lexical processing, and executive functioning are included as co-factors. Simply put, damage to those regions results in non-fluent aphasic speech; when they are undamaged, fluent aphasias result.

Recently, two different white matter regions that support speech fluency have been identified: the aslant tract and the anterior segment of the arcuate fasciculus (ASAF). The role of the ASAF was demonstrated in patients with post-stroke aphasia, while the role of the aslant tract shown in primary progressive aphasia. Regional white matter integrity appears to be crucial for speech production; however, the degree that each region exerts an independent influence on speech fluency is unclear. Furthermore, it is not yet defined if damage to both white matter regions influences speech in the context of the same neural mechanism (stroke-induced aphasia). This study assessed the relationship between speech fluency and quantitative integrity of the aslant region and the ASAF. It also explored the relationship between speech fluency and other white matter regions underlying classic cortical language areas such as the uncinate fasciculus and the inferior longitudinal fasciculus (ILF). Damage to these regions, except the ILF, was associated with speech fluency, suggesting synergistic association of these regions with speech fluency in post-stroke aphasia. These observations support the theory that speech fluency requires the complex, orchestrated activity between a network of pre-motor, secondary, and tertiary associative cortices, supported in turn by regional white matter integrity.

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Despite being perhaps the most studied form of aphasia, the critical lesion location for Broca's aphasia has long been debated, and in chronic patients, cortical damage often extends far beyond Broca's area. In a group of 70 patients, we examined brain damage associated with Broca's aphasia using voxel-wise lesion-symptom mapping (VLSM). We found that damage to the posterior portion of Broca's area, the pars opercularis, is associated with Broca's aphasia. However, several individuals with other aphasic patterns had considerable damage to pars opercularis, suggesting that involvement of this region is not sufficient to cause Broca's aphasia. When examining only individuals with pars opercularis damage, we found that patients with Broca's aphasia had greater damage in the left superior temporal gyrus (STG; roughly Wernicke's area) than those with other aphasia types. Using discriminant function analysis and logistic regression, based on proportional damage to the pars opercularis and Wernicke's area, to predict whether individuals had Broca's or another types of aphasia, over 95% were classified correctly. Our findings suggest that persons with Broca's aphasia have damage to both Broca's and Wernicke's areas, a conclusion that is incongruent with classical neuropsychology, which has rarely considered the effects of damage to both areas.

Gross tumor volume (GTV) segmentation is a critical step in esophageal cancer radiotherapy treatment planning. Inconsistencies across oncologists and prohibitive labor costs motivate automated approaches for this task. However, leading approaches are only applied to radiotherapy computed tomography (RTCT) images taken prior to treatment. This limits the performance as RTCT suffers from low contrast between the esophagus, tumor, and surrounding tissues. In this paper, we aim to exploit both RTCT and positron emission tomography (PET) imaging modalities to facilitate more accurate GTV segmentation. By utilizing PET, we emulate medical professionals who frequently delineate GTV boundaries through observation of the RTCT images obtained after prescribing radiotherapy and PET/CT images acquired earlier for cancer staging. To take advantage of both modalities, we present a two-stream chained segmentation approach that effectively fuses the CT and PET modalities via early and late 3D deep-network-based fusion. Furthermore, to effect the fusion and segmentation we propose a simple yet effective progressive semantically nested network (PSNN) model that outperforms more complicated models. Extensive 5-fold cross-validation on 110 esophageal cancer patients, the largest analysis to date, demonstrates that both the proposed two-stream chained segmentation pipeline and the PSNN model can significantly improve the quantitative performance over the previous state-of-the-art work by 11% in absolute Dice score (DSC) (from 0.654 ± 0.210 to 0.764 ± 0.134) and, at the same time, reducing the Hausdorff distance from 129 ± 73mm to 47 ± 56mm.