In previous research in small sample sizes, neuroanatomists noticed shallow sulci in this cortical expanse, but did not describe them beyond including an unlabeled sulcus in their figures and did not consider individual differences (Supplementary Methods and Supplementary Figs. 1–4 for historical details). In the present study, we fully update this sulcal landscape considering these overlooked indentations. In addition to defining the 13 sulci previously described within the LPC/LPOJ, as well as the posterior superior temporal cortex in individual participants (Methods; Petrides, 2019), we could also identify as many as four small and shallow PTS situated within the LPC/LPOJ that were highly variable across individuals and left undescribed until now (Supplementary Methods and Supplementary Figs. 1–4). Though we officially name and characterize features of these sulci in this paper for the first time, it is necessary to note that the location of these four sulci is consistent with the presence of variable “accessory sulci” in this cortical expanse mentioned in prior modern and classic studies (Supplementary Methods). For four example hemispheres with these 13-17 sulci identified, see Fig. 1a (Supplementary Fig. 5 for all hemispheres).

Macroanatomically, we could identify two sulci between the cSTS3 and the IPS-PO/lTOS ventrally and two sulci between the cSTS2 and the pips/IPS dorsally. Ventrally, we refer to these sulci as ventral (slocs-v; sulcus 6 in Fig. 1) and dorsal (slocs-d; sulcus 7 in Fig. 1) components of the supralateral occipital sulcus (slocs). The slocs-v, located between the posterior cSTS3 and lTOS, was present in 98.6% of hemispheres (left hemisphere: N = 71/72; right hemisphere: N = 71/72; Fig. 1). Conversely, the more variable slocs-d, located between the cSTS3 and IPS-PO, was present 68.0% of the time (left hemisphere: N = 50/72; right hemisphere: N = 48/72; Fig. 1). Dorsally, we refer to the other newly described sulci as the ventral (pAngs-v; sulcus 8 in Fig. 1) and dorsal (pAngs-d; sulcus 9 in Fig. 1) components of the posterior angular sulcus (pAngs). The pAngs components were more rare than the slocs components. Specifically, pAngs-v, located between cSTS2 and pips, was identifiable 31.3% of the time (19 left and 26 right hemispheres; Fig. 1). Located between cSTS2 and the IPS, pAngs-d was identifiable only 13.2% of the time (8 left and 11 right hemispheres; Fig. 1). These incidence rates were significantly different (GLM, main effect of sulcus: χ2(3)L=L166.53, p < .0001; no hemispheric effects: ps > .68). The slocs-v was more common than the other three sulci (ps < .0001), slocs-d was more common than the pAngs components (ps < .0001), and pAngs-v was more common than pAngs-d (p = .002). We could also identify these sulci in post-mortem hemispheres (Supplementary Figs. 2, 3), ensuring that these sulci were not an artifact of the cortical reconstruction process.

Beyond characterizing the incidence of sulci, it is also common in the neuroanatomical literature to qualitatively characterize sulci on the basis of fractionation and intersection with surrounding sulci (termed “sulcal types”; for examples in other cortical expanses, see Chiavaras and Petrides, 2000; Drudik et al., 2023; Miller et al., 2021; Paus et al., 1996; Weiner et al., 2014; Willbrand et al., 2022a). All four sulci most commonly did not intersect with other sulci (see Supplementary Tables 1–4 for a summary of the sulcal types of the slocs and pAngs dorsal and ventral components). The sulcal types were also highly comparable between hemispheres (rs > .99, ps < .001).

Given that sulcal incidence and patterning is also sometimes related to demographic features (Cachia et al., 2021; Leonard et al., 2009; Wei et al., 2017), subsequent GLMs relating the incidence and patterning of the three more variable sulci (slocs-d, pAngs-v, and pAngs-d) to demographic features (age and gender) revealed no associations for any sulcus (ps > .05). Finally, to help guide future research on these newly- and previously-classified LPC/LPOJ sulci, we generated probabilistic maps of each of these 17 sulci and share them with the field with the publication of this paper (Fig. 2; Data availability).

Given that the slocs-v was present in the majority of participants (98.6% across hemispheres) and the other three sulci were far more variable (<70% of hemispheres), we focused our analyses on this stable sulcal feature of the LPOJ. To do so, we first tested whether the slocs-v was morphologically (depth and surface area) and architecturally (gray matter thickness and myelination) distinct from the two sulci surrounding it: the cSTS3 and lTOS (Fig. 1). An rm-ANOVA (within-participant factors: sulcus, metric, and hemisphere for standardized metric units) revealed a sulcus x metric interaction (F(4, 276.19) = 179.15, η2 = 0.38, p < .001). Post hoc tests showed four main differences: (i) the slocs-v was shallower than cSTS3 (p < .001) but not lTOS (p = .60), (ii) the slocs-v was smaller than both the cSTS3 and lTOS (ps < .001), (iii) the slocs-v showed thicker gray matter than both the cSTS3 and lTOS (ps < .001), and iv) the slocs-v was less myelinated than both the cSTS and lTOS (ps < .001; Fig. 3a). There was also a sulcus x metric x hemisphere interaction (F(4.20, 289.81) = 4.16, η2 = 0.01, p = .002; hemispheric effects discussed in Supplementary Results). The morphological and architectural features of all LPC/LPOJ sulci are described in Supplementary Fig. 6.

We then tested whether the slocs-v was also functionally distinct from the cSTS3 and lTOS by leveraging resting-state network parcellations for each individual participant to quantify “connectivity fingerprints” for each sulcus in each hemisphere of each participant (Methods; Kong et al., 2019). An rm-ANOVA (within-participant factors: sulcus, network, and hemisphere for Dice coefficient overlap) revealed a sulcus x network interaction (F(32, 2144) = 80.18, η2 = 0.55, p < .001). Post hoc tests showed that this interaction was driven by four effects: (i) the cSTS3 overlapped more with the Default A subnetwork than both the slocs-v and lTOS (ps < .001), (ii) the slocs-v overlapped more with the Default C subnetwork than the lTOS (p < .001) and marginally than the cSTS3 (p = .077), (iii) the slocs-v overlapped more with the Dorsal Attention A subnetwork than both the cSTS3 and lTOS (ps < .001), and iv) the lTOS overlapped more with the Visual A and Visual B subnetworks than both the cSTS3 and slocs-v (ps < .004; Fig. 3b). There was also a sulcus x network x hemisphere interaction (F(32, 2144) = 3.99, η2 = 0.06, p < .001; hemispheric effects discussed in Supplementary Results). Together, these results indicate that the slocs-v is a morphologically, architecturally, and functionally distinct structure from its sulcal neighbors, and thus, deserves a distinct neuroanatomical definition.

We further found that the three caudal STS rami (Petrides, 2019; Segal and Petrides, 2012) and intermediate parietal sulci (aipsJ and pips; Petrides, 2019; Zlatkina and Petrides, 2014) are morphologically, architecturally, and functionally distinct structures for the first time (to our knowledge), which empirically supports their distinctions with separate sulcal labels (Supplementary Results and Supplementary Fig. 7).

Finally, leveraging a data-driven approach of cross-validated LASSO feature selection, we sought to determine whether sulcal depth, a main defining feature of sulci, related to cognitive performance (Methods). To do so, we primarily focused on spatial orientation and reasoning given that these abilities recruit multiple subregions of lateral parietal and/or occipital cortices (Gur et al., 2000; Karnath, 1997; Vendetti and Bunge, 2014; Wendelken, 2014). As in prior work (Maboudian et al., 2024; Voorhies et al., 2021; Willbrand et al., 2023b; Yao et al., 2022), we chose the model at the alpha that minimized MSE_cv. Participants with a slocs-v in both hemispheres and all behavioral metrics were included (N = 69). Due to their rarity (being in less than 70% of hemispheres at most), we did not include the slocs-d or pAng components in this analysis.

This method revealed an association between spatial orientation scores and normalized sulcal depth in the left hemisphere (MSE_cv = 25.63, alpha = 0.05; Fig. 4a), but not in the right hemisphere (MSE_cv = 26.41, alpha = 0.3). Further, we found that no LPC/LPOJ sulci were selected for reasoning in either hemisphere (right: alpha = 0.3, MSE = 24.01; left: alpha = 0.3, MSE = 24.01). Six left hemisphere LPC/LPOJ sulci were related to spatial orientation task performance (Fig. 4a, b). Four of these sulci were positioned ventrally: cSTS3 (β = −9.77), slocs-v (β = −3.36), lTOS (β = −4.91), and mTOS (β = −0.06), whereas two were positioned dorsally: pips (β = 5.02), and SPS (β = 4.30; Fig. 4a, b). Using LooCV to construct models that predict behavior, the LASSO-selected model explained variation in spatial orientation score (R²_cv = 0.06, MSE_cv = 23.99) above and beyond a model with all left hemisphere sulci (R² < 0.01, MSE_cv = 27.12). This model also showed a moderate correspondence (r_s = 0.29, p = .01; Fig. 4c) between predicted and actual measured scores. We then tested for anatomical and behavioral specificity using the AIC, which revealed two primary findings. First, we found that the LASSO-selected sulcal depth model outperformed a model using the cortical thickness of the six LASSO-selected sulci (R²_cv < .01, MSE_cv = 26.02, AIC_{cortical thickness} – AIC_{sulcal depth} = 2.19). This model also showed task specificity as these sulci outperformed a model with processing speed (R²_cv < .01, MSE_cv = 254.65, AIC_{processing speed} – AIC_{spatial orientation} = 63.57). Thus, our data-driven model explains a significant amount of variance on a spatial orientation task and shows behavioral and morphological specificity.

Finally, as in prior work examining variably-present PTS in other cortical expanses (for example, Amiez et al., 2018; Cachia et al., 2014; Fornito et al., 2004; Willbrand et al., 2024b), we assessed whether the presence/absence of the more variable PTS identified in the present work (slocs-d, pAngs-v, and pAngs-d) was related to spatial orientation, reasoning, and processing speed task performance. We identified no significant associations between the presence/absence of these sulci in either hemisphere with performance on these tests (ps > .05).

In the present study, we examined the relationship between LPC/LPOJ sulcal morphology, functional connectivity fingerprints, and cognition. We report five main findings. First, while manually defining sulci in LPC/LPOJ across 144 hemispheres, we uncovered four small and shallow sulci that are not included in present or classic neuroanatomy atlases or neuroimaging software packages. Second, we found that the most common of these structures (the slocs-v; identifiable 98.6% of the time) was morphologically, architecturally, and functionally differentiable from nearby sulci. Third, using a model-based, data-driven approach quantifying the relationship between sulcal morphology and cognition, we found a relationship between the depths of six LPC/LPOJ sulci and performance on a spatial orientation processing task. Fourth, the model identified distinct dorsal and ventral sulcal networks in LPC/LPOJ: ventral sulci had negative weights while dorsal sulci had positive weights (Fig. 4b). These findings are consistent with previous neuroimaging work from Gur et al. (2000) who demonstrated separate functional activations in dorsal parietal and the more ventrally situated occipital-parietal cortices for the judgment of line orientation task used in the present study. Fifth, the model identified that the slocs-v is cognitively relevant, further indicating the importance of this neuroanatomical structure. In the sections below, we discuss (i) the slocs-v relative to modern functional and cytoarchitectonic parcellations in the LPC/LPOJ, as well as anatomical connectivity to other parts of the brain, (ii) underlying anatomical mechanisms relating sulcal morphology and behavior more broadly, and (iii) limitations of the present study. Implications for future studies are distributed throughout each section.

To lay the foundation for future studies relating the newly-described slocs-v to different anatomical and functional organizational features of LPC/LPOJ, we situate probabilistic predictions of slocs-v relative to probabilistic cortical areas identified using multiple modalities. For example, when examining the correspondence between the slocs-v and modern multimodal (Human Connectome Project multimodal parcellation, HCP-MMP; Glasser et al., 2016) and observer-independent cytoarchitectural (Julich-Brain atlas; Amunts et al., 2020) areas (Methods), the slocs-v is located within distinct areas. In particular, the slocs-v aligns with the multimodally- and cytoarchitecturally-defined area PGp bilaterally and cytoarchitecturally-defined hIP4 in the right hemisphere (Fig. 5). In classic neuroanatomical terms (Cunningham, 1892), this indicates that the slocs-v is a putative “axial sulcus” for these regions, which future work can assess with analyses in individual participants.

Aside from recent multimodal and observer-independent cytoarchitectonic parcellations, an immediate question is: What is the relationship between the slocs-v and other functional regions at this junction between the occipital and parietal lobes, as well as potential anatomical connectivity? For example, there are over a dozen visual field maps in the cortical expanse spanning the TOS, IPS-PO, and the IPS proper (see (i), (ii), and (iii), respectively in Fig. 6a; Mackey et al., 2017). When projecting probabilistic locations of retinotopic maps from over 50 individuals from Wang and colleagues (Wang et al., 2015; Methods), the slocs-v is likel located outside of visual field maps extending into this cortical expanse (Fig. 6a). Nevertheless, when also projecting the map of the mean R² metric from the HCP retinotopy dataset from 181 participants shared by Benson and colleagues (Benson et al., 2018; Methods), the slocs-v is in a cortical expanse that explains a significant amount of variance (left hemisphere: R² = 19.29, R²_max = 41.73; right hemisphere: R²_mean = 21.17, R²_max = 44.23; Fig. 6b).

In terms of anatomical connectivity, as the slocs-v co-localizes with cytoarchitectonically defined PGp (Fig. 5) and previous studies have examined the anatomical connectivity of the probabilistically defined PGp, we can glean insight regarding the anatomical connectivity of slocs-v from these previous studies (Caspers et al., 2011; Wang et al., 2012). This prior work showed that PGp was anatomically connected to temporo-occipital regions, other regions in the temporal lobe, middle and superior frontal cortex, as well as the inferior frontal cortex and insula (Caspers et al., 2011; Wang et al., 2012). Furthermore, the slocs-v appears to lie at the junction of scene-perception and place-memory activity (a transition that also consistently co-localizes with the HCP-MMP area PGp) as identified by Steel and colleagues (2021). Of course, the location of the slocs-v relative to multimodal, cytoarchitectonic, and retinotopic areas, as well as the anatomical connectivity of the slocs-v, would need to be examined in individual participants, but the present work makes clear predictions for future studies as fleshed out here. To conclude this section, as the multimodal area PGp (Fig. 5) was recently proposed as a “transitional area” by Glasser and colleagues (2016; Supplementary Table 5), future studies can also further functionally and anatomically test the transitional properties of slocs-v.

In this section, we discuss potential anatomical mechanisms contributing to the relationship between sulcal depth and behavior in two main ways. First, long-range white matter fibers have a gyral bias, while short-range white matter fibers have a sulcal bias in which some fibers project directly from the deepest points of a sulcus (Cottaar et al., 2021; Reveley et al., 2015; Schilling et al., 2018, 2023; Van Essen et al., 2014). As such, recent work hypothesized a close link between sulcal depth and short-range white matter properties (Bodin et al., 2021; Pron et al., 2021; Voorhies et al., 2021; Willbrand et al., 2023b; Yao et al., 2022): deeper sulci would reflect even shorter short-range white matter fibers, which would result in faster communication between local, cortical regions and in turn, contribute to improved cognitive performance. This increased neural efficiency could underlie individual differences in cognitive performance. Ongoing work is testing this hypothesis which can be further explored in future studies incorporating anatomical, functional, and behavioral measures, as well as computational modeling.

Second, our model-based approach identified separate dorsal and ventral sulcal networks in which deeper sulci dorsally and shallower sulci ventrally contributed to the most explained variance on the spatial orientation task. A similar finding was identified by our previous work in the lateral prefrontal cortex (Yao et al., 2022). These previous and present findings may be explained by the classic anatomical compensation theory, which proposes that the size and depth of a sulcus counterbalance those of the neighboring sulci (Armstrong et al., 1995; Connolly, 1950; Zilles et al., 2013). Thus, a larger, deeper sulcus would be surrounded by sulci that are smaller and shallower, rendering the overall degree of cortical folding within a given region approximately equal (Armstrong et al., 1995; Connolly, 1950; Zilles et al., 2013). Future work can incorporate underlying white matter architecture into the compensation theory, as well as a recent modification that proposed to also incorporate local morphological features such as the deepest sulcal point (for example, sulcal pit or sulcal root; Régis et al., 2005), which has recently been shown to be related to different functional features of the cerebral cortex (Bodin et al., 2018; Leroy et al., 2015; Natu et al., 2021). Altogether, these and recent findings begin to build a multimodal mechanistic neuroanatomical understanding underlying the complex relationship between sulcal depth and cognition relative to other anatomical features.

The main limitation of our study is that presently, the most accurate methodology to define sulci —especially the small, shallow, and variable PTS—requires researchers to manually trace each structure on the cortical surface reconstructions. This method is limited due to the individual variability of cortical sulcal patterning (Fig. 1, Supplementary Fig. 5), which makes it challenging to identify sulci without extensive experience and practice. However, we anticipate that our probabilistic maps (Fig. 2) will provide a starting point and hopefully, expedite the identification of these sulci in new participants. This should accelerate the process of subsequent studies confirming the accuracy of our updated schematic of LPC/LOPJ. This manual method is also arduous and time-consuming, which, on the one hand, limits the sample size in terms of number of participants, while on the other, results in thousands of precisely defined sulci. This push-pull relationship reflects a broader conversation in the human brain mapping and cognitive neuroscience fields between a balance of large N studies and “precision imaging” studies in individual participants (Gratton et al., 2022; Naselaris et al., 2021; Rosenberg and Finn, 2022). Though our sample size is comparable to other studies that produced reliable results relating sulcal morphology to brain function and cognition (for example, Cachia et al., 2021; Garrison et al., 2015; Lopez-Persem et al., 2019; Miller et al., 2021; Roell et al., 2021; Voorhies et al., 2021; Weiner, 2019; Willbrand et al., 2022a, 2022b; Yao et al., 2022), ongoing work that uses deep learning algorithms to automatically define sulci should result in much larger sample sizes in future studies (Borne et al., 2020; Lee et al., 2024, 2025; Lyu et al., 2021). The time-consuming manual definitions of primary, secondary, and PTS also limit the cortical expanse explored in each study, thus restricting the present study to LPC/LPOJ.

Additionally, the scope of the present study is limited in that the sample was only in young adults. This sample was selected as it is the standard of the field when charting features of PTS for the first time (for example, Chiavaras and Petrides, 2000; Drudik et al., 2023; Miller et al., 2021; Paus et al., 1996; Segal and Petrides, 2012; Sprung-Much and Petrides, 2018; Willbrand et al., 2023c, 2022a; Zlatkina and Petrides, 2014). Nevertheless, it is necessary to explore how well this updated sulcal landscape translates to different age groups, species, and clinical populations.

It is also worth noting that the morphological-behavioral relationship identified in the present study explains a modest amount of variance; however, the more important aspect of our findings is that multiple sulci identified in our model-based approach are recently-characterized sulci in LPC/LOPJ identified by our group and others (Petrides, 2019), and thus, the relationship would have been overlooked or lost if these sulci were not identified. Finally, although we did not focus on the relationship between the other three PTS (slocs-d, pAngs-v, and pAngs-d) to anatomical and functional features of LPC and cognition, given that variability in sulcal incidence is cognitively (for review see, Cachia et al., 2021), anatomically (Amiez et al., 2021; Vogt et al., 1995), functionally (Lopez-Persem et al., 2019), and translationally (Clark et al., 2010; Le Provost et al., 2003; Meredith et al., 2012; Nakamura et al., 2020; Yücel et al., 2003, 2002) relevant, future work can also examine the relationship between the more variable slocs-d, pAngs-v, and pAngs-d and these features.

In conclusion, we uncovered four previously-undefined sulci in LPC/LPOJ and quantitatively showed that the slocs-v is a stable sulcal landmark that is morphologically, architecturally, and functionally differentiable from surrounding sulci. We further used a data-driven, model-based approach relating sulcal morphology to behavior, which identified different relationships of ventral and dorsal LPC/LPOJ sulcal networks contributing to the perception of spatial orientation. The model identified the slocs-v, further indicating the importance of this newly-described neuroanatomical structure. Altogether, this work provides a scaffolding for future “precision imaging” studies interested in understanding how anatomical and functional features of LPC/LPOJ relate to cognitive performance at the individual level.

Data for the young adult human cohort analyzed in the present study were from the Human Connectome Project (HCP) database (https://www.humanconnectome.org/study/hcp-young-adult/overview). Here, we used 72 randomly-selected participants, balanced for gender (following the terminology of the HCP data dictionary), from the HCP database (50% female, 22-36 years old, and 90% right-handed; there was no effect of handedness on our behavioral tasks; Supplementary Methods) that were also analyzed in several prior studies (Hathaway et al., 2023; Maboudian et al., 2024; Miller et al., 2021, 2020; Willbrand et al., 2024a, 2023b, 2023c, 2022a). HCP consortium data were previously acquired using protocols approved by the Washington University Institutional Review Board (Mapping the Human Connectome: Structure, Function, and Heritability; IRB # 201204036). Informed consent was obtained from all participants.

Anatomical T1-weighted (T1-w) MRI scans (0.8 mm voxel resolution) were obtained in native space from the HCP database. Reconstructions of the cortical surfaces of each participant were generated using FreeSurfer (v6.0.0), a software package used for processing and analyzing human brain MRI images (surfer.nmr.mgh.harvard.edu; Dale et al., 1999; Fischl et al., 1999). All subsequent sulcal labeling and extraction of anatomical metrics were calculated from these native space reconstructions generated through the HCP’s version of the FreeSurfer pipeline (Glasser et al., 2013).

In addition to structural and functional neuroimaging data, the HCP also includes a wide range of behavioral metrics from the NIH toolbox (Barch et al., 2013). To relate LPC/LPOJ sulcal morphology to behavior, we leveraged behavioral data related to spatial orientation (Variable Short Penn Line Orientation Test), relational reasoning (Penn Progressive Matrices Test), and processing speed (Pattern Completion Processing Speed Test; Supplementary Methods for task details). We selected these tasks as previous functional neuroimaging studies have shown the crucial role of LPC/LPOJ in relational reasoning and spatial orientation (Gur et al., 2000; Karnath, 1997; Vendetti and Bunge, 2014; Wendelken, 2014), while our previous work relating sulcal morphology to cognition uses processing speed performance as a control behavioral task (Voorhies et al., 2021; Willbrand et al., 2024a, 2022b).

To determine if the slocs-v is functionally distinct from surrounding sulci, we generated functional connectivity profiles using recently developed analyses (Miller et al., 2021; Willbrand et al., 2023a, 2022a). First, we used resting-state network parcellations for each individual participant from Kong and colleagues (2019), who generated individual network definitions by applying a hierarchical Bayesian network algorithm to produce maps for each of the 17 networks in individual HCP participants. Importantly, this parcellation was conducted blind to both cortical folding and our sulcal definitions. Next, we resampled the network profiles for each participant onto the fsaverage cortical surface, and then to each native surface using CBIG tools (https://github.com/ThomasYeoLab/CBIG). We then calculated the spatial overlap between a sulcus and each of the 17 individual resting-state networks via the Dice coefficient (Equation 1): This process of calculating the overlap between each sulcus and the 17-network parcellation generated a “connectivity fingerprint” for each sulcus in each hemisphere of each participant. We then ran an rm-ANOVA with within-participant factors of sulcus (slocs-v, cSTS3, and lTOS), network (17 networks), and hemisphere (left and right) to determine if the network profiles (i.e., the Dice coefficient overlap with each network) of the slocs-v was differentiable from the surrounding sulci (i.e., cSTS3 and lTOS). Here we discuss effects related to networks that at least showed minor overlap with one sulcus (i.e., Dice ≥ .10). As in the prior analysis, we also repeated these analyses for the three cSTS components and the two intermediate parietal sulcal components (Supplementary Results and Supplementary Fig. 7).

To putatively relate the slocs-v to modern multimodal (HCP multimodal parcellation, HCP-MMP; Glasser et al., 2016) and cytoarchitectural (Julich-Brain atlas; Amunts et al., 2020) regions of the cerebral cortex located in fsaverage template space, we quantified the Dice coefficient overlap between the slocs-v of each participant (resampled to fsaverage space) and the individual regions of interest comprising the HCP-MMP and Julich-Brain parcellations.

To assess whether any of the LPC/LPOJ sulci related to retinotopic representations, we leveraged population receptive field mapping data (Benson et al., 2018). For each sulcal MPM (as the retinotopic data were only available in this template space), we extracted the mean R² values (i.e., the percentage of variance in each vertex explained by the population receptive field model) for vertices that showed meaningful retinotopic responses across participants (thresholded at R² > 10%; Mackey et al., 2017).