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The ventral visual stream underlies key human visual object recognition abilities. However, neural encoding in the higher areas of the ventral stream remains poorly understood. Here, we describe a modeling approach that yields a quantitatively accurate model of inferior temporal (IT) cortex, the highest ventral cortical area. Using high-throughput computational techniques, we discovered that, within a class of biologically plausible hierarchical neural network models, there is a strong correlation between a model's categorization performance and its ability to predict individual IT neural unit response data. To pursue this idea, we then identified a high-performing neural network that matches human performance on a range of recognition tasks. Critically, even though we did not constrain this model to match neural data, its top output layer turns out to be highly predictive of IT spiking responses to complex naturalistic images at both the single site and population levels. Moreover, the model's intermediate layers are highly predictive of neural responses in the V4 cortex, a midlevel visual area that provides the dominant cortical input to IT. These results show that performance optimization—applied in a biologically appropriate model class— can be used to build quantitative predictive models of neural processing.

Human visual object recognition is subserved by a multitude of cortical areas. To make sense of this system, one line of research focused on response properties of primary visual cortex neurons and developed theoretical models of a set of canonical computations such as convolution, thresholding, exponentiating and normalization that could be hierarchically repeated to give rise to more complex representations. Another line or research focused on response properties of high-level visual cortex and linked these to semantic categories useful for object recognition. Here, we hypothesized that the panoply of visual representations in the human ventral stream may be understood as emergent properties of a system constrained both by simple canonical computations and by top-level, object recognition functionality in a single unified framework (Yamins et al., 2014; Khaligh-Razavi and Kriegeskorte, 2014; Guclu and van Gerven, 2015). We built a deep convolutional neural network model optimized for object recognition and compared representations at various model levels using representational similarity analysis to human functional imaging responses elicited from viewing hundreds of image stimuli. Neural network layers developed representations that corresponded in a hierarchical consistent fashion to visual areas from V1 to LOC. This correspondence increased with optimization of the model's recognition performance. These findings support a unified view of the ventral stream in which representations from the earliest to the latest stages can be understood as being built from basic computations inspired by modeling of early visual cortex shaped by optimization for high-level object-based performance constraints.

Purpose The FLAME trial provides strong evidence that MR-guided external beam radiation therapy (EBRT) focal boost for localized prostate cancer increases biochemical disease-free survival (bDFS) without increasing toxicity. Yet, there are many barriers to implementation of focal boost. Our objectives are to systemically review clinical outcomes for MR-guided EBRT focal boost and to consider approaches to increase implementation of this technique. Methods We conducted literature searches in four databases according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guideline. We included prospective phase II/III trials of patients with localized prostate cancer underdoing definitive EBRT with MR-guided focal boost. The outcomes of interest were bDFS and acute/late gastrointestinal and genitourinary toxicity. Results Seven studies were included. All studies had a median follow-up of greater than 4 years. There were heterogeneities in fractionation, treatment planning, and delivery. Studies demonstrated effectiveness, feasibility, and tolerability of focal boost. Based on the Phoenix criteria for biochemical recurrence, the reported 5-year biochemical recurrence-free survival rates ranged 69.7–100% across included studies. All studies reported good safety profiles. The reported ranges of acute/late grade 3 + gastrointestinal toxicities were 0%/1–10%. The reported ranges of acute/late grade 3 + genitourinary toxicities were 0–13%/0–5.6%. Conclusions There is strong evidence that it is possible to improve oncologic outcomes without substantially increasing toxicity through MR-guided focal boost, at least in the setting of a 35-fraction radiotherapy regimen. Barriers to clinical practice implementation are addressable through additional investigation and new technologies.

Background There is substantial unexplained geographical and surgeon-to-surgeon variation in rates of surgery. One would expect surgeons to treat patients and themselves similarly based on best evidence and accounting for patient preferences. Questions/purposes (1) Are surgeons more likely to recommend surgery when choosing for a patient than for themselves? (2) Are surgeons less confident in deciding for patients than for themselves? Methods Two hundred fifty-four (32%) of 790 Science of Variation Group (SOVG) members reviewed 21 fictional upper extremity cases (eg, distal radius fracture, De Quervain tendinopathy) for which surgery is optional answering two questions: (1) What treatment would you choose/recommend: operative or nonoperative? (2) On a scale from 0 to 10, how confident are you about this decision? Confidence is the degree that one believes that his or her decision is the right one (ie, most appropriate). Participants were orthopaedic, trauma, and plastic surgeons, all with an interest in treating upper extremity conditions. Half of the participants were randomized to choose for themselves if they had this injury or illness. The other half was randomized to make treatment recommendations for a patient of their age and gender. For the choice of operative or nonoperative, the overall recommendation for treatment was expressed as a surgery score per surgeon by dividing the number of cases they would operate on by the total number of cases (n = 21), where 100% is when every surgeon recommended surgery for every case. For confidence, we calculated the mean confidence for all 21 cases per surgeon; overall score ranges from 0 to 10 with a higher score indicating more confidence in the decision for treatment. Results Surgeons were more likely to recommend surgery for a patient (44.2% ± 14.0%) than they were to choose surgery for themselves (38.5% ± 15.4%) with a mean difference of 6% (95% confidence interval [CI], 2.1%–9.4%; p = 0.002). Surgeons were more confident in deciding for themselves than they were for a patient of similar age and gender (self: 7.9 ± 1.0, patient: 7.5 ± 1.2, mean difference: 0.35 [CI, 0.075–0.62], p = 0.012). Conclusions Surgeons are slightly more likely to recommend surgery for a patient than they are to choose surgery for themselves and they choose for themselves with a little more confidence. Different perspectives, preferences, circumstantial information, and cognitive biases might explain the observed differences. This emphasizes the importance of (1) understanding patients’ preferences and their considerations for treatment; (2) being aware that surgeons and patients might weigh various factors differently; (3) giving patients more autonomy by letting them balance risks and benefits themselves (ie, shared decision-making); and (4) assessing how dispassionate evidence-based decision aids help inform the patient and influences their decisional conflict. Level of Evidence Level III, diagnostic study.