Association Between Urban Upbringing and Cortical Gyrification in Persons with Schizophrenia

Abstract

Background:

Many studies suggest that urban upbringing might increase the risk of developing schizophrenia (SCZ). However, the precise brain changes associated with urban upbringing remain poorly understood. In this study, we investigated how urban upbringing might influence cortical gyrification, a brain feature that reflects early structural development.

Methods:

The study included 70 Healthy Controls (HC) and 87 individuals diagnosed with SCZ, all aged between 18 and 50 years. Participants and their caregivers were interviewed to collect information about birthplace and upbringing location. Based on data from the Indian Census (1971–2011), upbringing locations were categorized into three groups: rural, town, and city. An urbanicity index was calculated using a previously established method. Brain anatomical MRI images were processed using FreeSurfer. Regression analysis was conducted using the QDEC interface, with the gyrification index (GI) as the dependent variable, and urbanicity index, sex, and age as predictors.

Results:

In the entire sample, a significant positive association was observed between the urbanicity index and the GI in the left supramarginal gyrus (BA40; p = .001), left rostral middle frontal gyrus (BA10; p < .001), and both the left and right lateral occipital gyri (BA18; p = .001). Additionally, a significant interaction effect between the diagnosis and urbanicity index was found in multiple brain regions.

Conclusions:

These findings suggest that urban living has a significant influence on brain development. Identifying such risk factors and underlying mechanisms could help develop prevention strategies and guide improvements in urban planning.

Keywords

Cortical gyrification schizophrenia urbanicity index urban upbringing MRI

Key Messages

In individuals with schizophrenia, the urbanicity index showed a significant association with the gyrification index in the left rostral middle frontal gyrus, left supramarginal gyrus, and bilateral lateral occipital gyri.

Recognizing urban upbringing as a risk factor for neurodevelopmental changes in schizophrenia may inform the development of preventive interventions.

Schizophrenia (SCZ) is a severe mental illness with complex causes involving genetic and environmental factors.^1–3 Urban birth and upbringing are key environmental risk factors linked to an increased likelihood of SCZ.^4–6 Exposure to urban settings during the first 15 years notably raises this risk.⁷ Family history and urban upbringing independently increase psychosis risk, but combined, they triple it.⁵ A meta-analysis found urban upbringing raises SCZ risk by approximately 2.37 times.⁶

Although previous studies have explored the link between urban upbringing and SCZ risk, the underlying neurobiological mechanisms remain unclear. Only a few structural and functional neuroimaging studies have investigated how urban environments influence brain development, particularly in individuals with SCZ.⁸ Research in healthy individuals has shown that urban upbringing is associated with reduced cortical thickness in the frontal and temporal lobes,⁸ decreased grey matter in the frontal and parietal regions, and altered white matter integrity in the superior longitudinal fasciculus.^{9, 10} Functional studies also report differences in activation of brain regions involved in stress and reward processing—such as the amygdala, prefrontal cortex, and perigenual anterior cingulate cortex—between urban and rural populations.^11–13 However, these findings are based on healthy samples and may not fully apply to SCZ. Limited research in SCZ suggests that an urban upbringing is associated with reduced grey matter volume, although this association is not consistently observed with cortical thickness.^{14, 15} One recent study also found altered amygdala activation during a trust game task in patients with psychosis who had urban backgrounds.¹⁶

It is important to note that few studies have examined the impact of urbanization in developing countries. Regions in Asia and Africa are undergoing rapid urban growth, and by 2050, nearly 70% of the global population is projected to live in cities.¹⁷ This unplanned urban expansion contributes to socio-economic inequality, social instability, strain on the health system, and economic risk.¹⁸ Although urbanization is often linked to increased SCZ risk, a recent study across 42 low- and middle-income countries found no clear association between urban living and psychotic disorders or experiences.^{19, 20} However, this study was cross-sectional, did not focus on urban upbringing, and excluded countries such as India and China, both of which are experiencing intense urban growth.^{21, 22} In China, no urban–rural differences in SCZ prevalence were found,²³ while India’s National Mental Health Survey (2015–2016) reported a higher SCZ prevalence in urban areas.²¹ Given that early-life environments significantly influence SCZ risk,⁷ ongoing urbanization in developing countries may have important implications. Nevertheless, the neurobiological effects of urban versus rural upbringing in these regions remain unstudied.

Environmental factors have a significant influence on cortical development during the perinatal period and early childhood.^24–27 Cortical gyrification—the folding of the brain’s surface—is a highly evolved, species- specific feature in humans.^{27, 28} Although previously thought to be complete by the third trimester,²⁹ recent evidence shows that gyrification continues postnatally, peaking in early childhood and gradually declining through adolescence.^30–34 Moreover, gyrification can be modified by environmental factors such as diving, musical training, and meditation, with studies showing dose- dependent effects.^35–37 The gyrification index (GI), which measures cortical folding, is calculated as the ratio of the pial surface to the brain’s perimeter in coronal sections; higher GI indicates more folding.³⁸ Modern 3D imaging techniques have enhanced the accuracy and localization of GI measurement.^38–40 GI is a sensitive marker of brain development. It is altered in neurodevelopmental disorders, including SCZ.⁴¹ While perinatal environmental factors have been shown to influence gyrification,²⁵ no studies have investigated how urban upbringing affects GI in individuals with SCZ.

In a previous study,⁴² we reported that urban birth and upbringing were associated with altered functional connectivity in brain regions involved in reward processing and social cognition in individuals with SCZ. Building on that work, the present study examines whether urban upbringing is associated with altered cortical gyrification in both healthy individuals and individuals with SCZ.

Methods

Study Participants

This study is a secondary analysis that combines data from three separate studies conducted between 2013 and 2019.^43–45 It includes information from 70 healthy controls (HCs) and 87 patients with SCZ, all aged between 18 and 50 years, based on DSM-IV criteria. Participants were not included in the study if they met any of these criteria such as: (a) presence of any additional axis I diagnosis, including substance use or dependence (except nicotine) within the past 12 months; (b) previous head injury, epilepsy, stroke, or left or mixed handedness as assessed by the Edinburgh Handedness Inventory⁴⁶; (c) intellectual or developmental disability; (d) the existence of metallic implants or objects that are paramagnetic within the body; (e) fear of enclosed/confined spaces; or (f) pregnancy or breastfeeding. Additionally, HCs were excluded if they reported a history of psychosis in their first-degree relatives or any history of psychiatric disorders.

Patients were recruited from clinical centers at the National Institute of Mental Health and Neurosciences, Bengaluru, while HCs were recruited through flyers and referrals. Every participant underwent an evaluation with the MacArthur Competence Assessment Tool for Clinical Research⁴⁷ to verify the ability to give informed consent. Ethical clearance was secured from the institutional ethics committee for all three studies (approval numbers: study 1 NIMHANS/85th IEC/2013, dated May 17, 2013; study 2 NIMHANS/105th IEC/2016, dated July 2, 2016; study 3 NIMHANS/105th IEC/2016, dated July 2, 2016). These studies were conducted by the principles outlined in the Declaration of Helsinki.

Patient psychopathology and functioning were assessed using the Positive and Negative Syndrome Scale (PANSS),⁴⁸ the Calgary Depression Scale for Schizophrenia (CDSS),⁴⁹ the Clinical Global Impression (CGI) scale,⁵⁰ the Global Assessment of Functioning (GAF),⁵¹ and the Structured Clinical Interview for DSM-IV Disorders.⁵² Urine samples from patients were screened for opiates, cocaine, sedative-hypnotics, and cannabis. A mental health professional conducted semi-structured interviews with both patients and their caregivers to collect demographic and clinical information. Specifically, detailed information on each patient’s birthplace and upbringing was obtained through discussions with the patients and their primary caregivers— usually parents or other adult family members familiar with the patient’s early life. Data included the exact birthplace (village, ward, taluk, district, and state) and upbringing details. For individuals who relocated before age 15, information on the year of migration, new location, and duration of residence was also recorded. In HCs, this information was gathered directly from the participants. Medication usage was documented, and anti-psychotic doses were converted to olanzapine equivalents.⁵³

Calculation of Urbanicity Index

Participants were classified into three groups based on their living environments: (a) rural areas, (b) towns, and (c) cities, using data from the Census of India collected between 1971 and 2011 (https://www.censusindia.gov.in/DigitalLibrary/Tables.aspx). Broadly, the city is characterized by a population of over a million, which includes megacities, urban agglomerations, and urban outgrowths. Towns were categorized by either statutory towns-recognized by having a municipality, corporation, cantonment board, or notified town area committee—or census towns, with a population of more than 5,000, with 75% or more of the male workforce engaged in economic activity that is not directly related to farming, and a population density of at least 400 people per square kilometer. Areas not meeting the criteria for statutory or census towns were classified as rural.

Additionally, we calculated a developmental urbanicity index for each participant using a method established in previous studies.^{8, 42} The urbanicity index quantifies the number of years an individual spends living in an urban environment during critical neurodevelopmental periods—childhood and adolescence.⁴² For each of the first 15 years of life, a score of 1 to 3 is assigned based on the living environment: rural upbringing (score = 1), town upbringing (score = 2), and city upbringing (score = 3). The urbanicity score is calculated by multiplying the score for each year by the number of years spent in that environment, then summing these values to produce a score ranging from 15 to 45. In a supplementary analysis focusing on birthplaces, towns and cities were grouped as urban, while all other locations were classified as rural.

MRI Data Collection and Analysis

The whole-brain anatomical images were obtained using a 3T MRI machine (Skyra, Siemens Healthcare, Germany). T1-weighted MPRAGE sequence acquisition parameters were listed as follows: repetition time (TR), 2,200 ms; echo time (TE), 2.45 ms; flip angle, 8°; matrix size, 256 × 256; slice thickness, 1 mm; voxel size, 1 mm³; and a total of 176 slices. Foam pads were used to reduce head movement, and participants with motion over 3 mm were removed from the final analysis.

Image processing was conducted in FreeSurfer 5.3 version (http://surfer.nmr.mgh.harvard.edu). The standard preprocessing steps included motion correction, affine alignment to a standard space with 12 degrees of freedom, correction for intensity inhomogeneity through non-uniform intensity normalization, and removal of non-brain tissue. Brain volume normalization was applied to match the intensity histogram of the FreeSurfer atlas, followed by nonlinear registration of each subject’s brain to the atlas. This enabled atlas-based segmentation and labelling of subcortical structures, brainstem, cerebellum, and cerebral cortex. The cortical surface of each hemisphere was standardized in coordinate space, with manual inspection performed to ensure accuracy. Subsequently, the brain was mapped onto a standard spherical coordinate system from the FreeSurfer atlas, allowing for automatic cortical parcellation by gyri. These parcellations were used to compute regional gyrification indices.

For statistical analysis, the Query, Design, Estimate, Contrast (QDEC) interface of FreeSurfer was used to aggregate data and generate cortical morphometric maps. Vertex-wise general linear models (GLM) were conducted to examine relationships between GI and variables, including urbanicity, age, and gender. Additionally, comparisons of GI between individuals born in rural versus urban settings were performed, controlling for age and sex. Family-wise error correction was implemented through a Monte Carlo simulation (MCS) with a significance threshold of 1.3 (p < .05) within the QDEC interface.

Statistical Analysis

Correlations between the urbanicity index and clinical disorganization and impoverishment scores were assessed using Spearman’s correlation. These clinical scores were derived following a recent meta-analysis.⁵⁴ The Shapiro-Wilk test was used to evaluate data normality, revealing that age and GAF scores were normally distributed. In contrast, the urbanicity index and years of education were not normally distributed. Group comparisons between SCZ patients and HCs were performed using independent t-tests for normally distributed variables and Mann-Whitney tests for non-normally distributed variables. Chi-square tests analyzed categorical variables. Clinical variables such as age of onset, illness duration, and PANSS subscale scores showed non-normal distributions, whereas total PANSS scores were normally distributed. Pearson’s correlation was used to analyze the association between urbanicity index and total PANSS scores, while Spearman’s correlation assessed relationships with other clinical measures.

Results

Between-group Analyses

Table 1 demonstrates the demographic and clinical characteristics of the whole study sample. Notable differences were observed between the two groups in terms of age, urbanicity index scores, and years of education, while no significant difference was found in sex distribution. Group comparisons revealed that individuals with SCZ exhibited significantly reduced gyrification in the left lateral occipital gyrus (p < .001), left inferior temporal gyrus (p < .001), and right precentral gyrus (p = .049) (Supplementary Figure S1 and Table S1). Given the age difference between groups, all analyses were adjusted for age as a covariate. The specific effect of age on the GI is reported in Supplementary Table S2.

Table 1.

Comparison of Socio-demographic and Clinical Characteristics Between Schizophrenia and Healthy Controls.

		Schizophrenia (n = 87)	Healthy Controls (n = 70)	t/U/χ²	p
Age^† M (SD)		31.9 (6.8)	27.2 (5.3)	4.8	.001*
Sex (Male: Female)^‡		76:11	58:12	0.6	.49
Urbanicity index score^§ M (SD)		25.2 (10.0)	28.8 (10.8)	2502.5	.04*
Socio-economic status, n (%)	Upper	27 (39.7)	6 (7.1)	29.5	<.001*
	Upper Middle	31 (45.6)	44 (51.8)
	Lower Middle	9 (13.2)	23(27.1)
	Upper Lower	1 (1.5)	12 (14.1)
Birthplace^‡ n (%)	Rural	43 (63.2)	25 (36.8)	2.1	.10
Birthplace^‡ n (%)	Urban	44 (49.4)	45 (50.6)	2.1	.10
Years of education^§ M (SD)		14.4 (2.8)	16.9 (3.6)	1838.0	<.001*
Age of onset, M (SD)		23.2 (4.9)	–	–	–
Duration of illness (months), M (SD)		93.9 (71.0)	–	–	–
PANSS Positive symptoms, M (SD)		12.4 (5.4)	–	–	–
PANSS Negative symptoms, M (SD)		13.3 (5.4)	–	–	–
PANSS General psychopathology, M (SD)		25.1 (5.9)	–	–	–
PANSS Total score, M (SD)		53.9 (14.1)	–	–	–
CDSS, M (SD)		1.4 (1.7)	–	–	–
GAF^† M (SD)		58.7 (16.6)	94.3 (3.3)	11.9	.001*

*p < .05 (2-tailed).

^†Independent t-test.

^‡Chi-squared test.

^§Mann-Whitney U test.

PANSS: Positive and Negative Syndrome Scale for Schizophrenia; CDSS: Calgary Depression Scale for Schizophrenia; GAF: Global Assessment of Functioning.

Association Between Urbanicity Index and Cortical Gyrification

Overall, a significant positive correlation was observed between the urbanicity index and GI in the left supramarginal gyrus (BA40; p = .001), left rostral middle frontal gyrus (BA10; p < .001), and both the left and right lateral occipital gyri (BA18; p = .001) (Table 2). In addition, a notable interaction was found between diagnosis and urbanicity in the right rostral middle frontal (BA46; p < .001), and inferior temporal cortex (BA19; p < .001), and left superior parietal cortex (BA7; p < .001) (Supplementary Table S3).

Subgroup analyses revealed that SCZ patients exhibited a considerable positive correlation between GI and urbanicity index in the right lateral orbitofrontal (BA11; p < .001) and rostral middle frontal cortex (BA46; p = .019). In HCs, noteworthy positive associations were found in the right lateral occipital gyrus (BA18; p < .001), left rostral middle frontal (BA10; p < .001), and lateral occipital gyrus (BA19; p < .001) (Table 2 and Figure 1).

Table 2.

Relationship Between Urbanicity Index and Whole Brain Gyrification Index (Controlling for Age and Sex and Corrected for Multiple Comparisons).

Group	Cluster Location	Number of Vertices	Size (mm²)	Peak Vertex MNI Coordinates (x, y, z)	Clusterwise p Value
All subjects (N = 157) (schizophrenia + healthy controls)	Left rostral middle frontal cortex	2,065	1444.39	–28, 54, –6	.0001*
	Left supramarginal gyrus	1,738	1269.36	–56, –37, 33	.0014*
	Left lateral occipital cortex	2,092	955.99	–24, –89, –13	.0001*
	Right lateral occipital cortex	3,230	2512.55	28, –96, –2	.0001*
Schizophrenia only (n = 87)	Right rostral middle frontal gyrus	1,359	778.91	39, 34, 15	.0199*
Schizophrenia only (n = 87)	Right lateral orbitofrontal cortex	1,725	1136.13	25, 40, –15	.0006*
Healthy controls only (n = 70)	Left rostral middle frontal cortex	2,700	1967.55	–30, 54, –5	.0001*
	Left lateral occipital cortex	1,620	1223.61	–26, –92, 15	.0001*
	Right lateral occipital cortex	1,860	1476.26	28, –96, –2	.0001*

*p < .05 (2-tailed).

MNI: Montreal Neurological Institute.

Figure 1.

Representative Image Showing the Positive Correlation of Gyrification Index and Urbanicity Index (p < .05, Corrected). a: Right lateral occipital gyrus in study sample; b: (i) left rostral middle frontal, (ii) left supramarginal, (iii) left lateral occipital in study sample; c: (i) right lateral orbitofrontal, (ii) right rostral middle frontal in schizophrenia; d: (i) left hemisphere rostral middle frontal, (ii) left hemisphere lateral occipital gyrus in HV; e: right hemisphere lateral occipital gyrus in HV.

Given that larger metropolitan areas may impose greater social stress than smaller cities, we conducted a sub-analysis distinguishing between metropolitan and non-metropolitan cities. Participants were reclassified into four categories based on their early-life environments: rural, town, non-metropolitan city, and metropolitan city, and were assigned scores of 1, 2, 3, and 4, respectively, for each year up to age 15. The results of this analysis are presented in Supplementary Table S4. Additionally, to account for potential confounding effects of education and current place of residence, we performed a further sub-analysis including years of education as an additional covariate. The findings from these analyses are reported in Supplementary Tables S5 and S6.

Effect of Birthplace on Gyrification

Significant differences in GI were ob- served between individuals with urban versus rural birth in several brain regions, including the left superior temporal gyrus (p < .001), left superior parietal gyrus (p < .001), left superior frontal gyrus (p < .001), left and right lateral occipital gyri (p < .001), left pars triangularis (p = .013), and right insula (p < .001). In all these regions, participants born in urban areas showed a higher GI compared to those born in rural areas. A significant interaction between diagnosis and birthplace was identified in the left precuneus (BA7; p < .001), left fusiform (BA20; p < .001), right superior temporal gyrus (p < .001), and right precuneus (BA31; p < .001) (Supplementary Table S7). Within the SCZ group, urban-born subjects had a significantly higher GI in the left precuneus (p < .001). The left superior temporal gyri (p < .001), but lower gyrification in the right superior frontal gyrus (p = .018). No significant effects of birthplace on gyrification were observed in HCs (Table 3).

Table 3.

Differences in Gyrification Index Between Urban and Rural Birthplaces (Controlling for Age and Sex and Corrected for Multiple Comparisons).

Group	Cluster Location	Number of Vertices	Size (mm²)	Peak Vertex MNI Coordinates (x, y, z)	Cluster-wise p Value	Effect Size (Cohen’s d)
All subjects (N = 157) (schizophrenia + healthy controls)	Left superior temporal cortex	9,451	4,214.25	–58, -21, –7	.0001*	0.085
	Left superior parietal cortex	8,839	3,940.93	–13, -67, 63	.0001*	0.191
	Left superior frontal cortex	2,821	1,781.14	–11, 67, 11	.0001*	0.094
	Left lateral occipital cortex	1,817	1,385.99	–20, –100, 0	.0001*	0.299
	Left pars triangularis	1,168	739.97	–47, 30, –1	.0131*	0.051
	Right insula	46,111	21,307.76	40, 6, –21	.0001*	0.078
	Right lateral occipital cortex	2,436	1,955.22	20, -101, –7	.0001*	0.263
Schizophrenia subjects only (n = 87)	Left precuneus	35,803	16,897.61	–8, -67, 55	.0001*	0.156
	Left superior temporal cortex	30,411	15,133.59	–58, -3, –12	.0001*	0.032
	Right superior frontal cortex	105,778	53,004.34	44, -7, –19	.0176*	0.099
	Right superior temporal cortex	1,368	707.88	9, 20, 58	.0001*	0.077

*p < .05 (2-tailed).

MNI: Montreal Neurological Institute.

Relationship Between Urbanicity Index, GI, and Clinical Parameters in SCZ

No significant relationship was found between the urbanicity index and clinical parameters, including age at onset, total PANSS score, or PANSS subscores (p > .05) (Supplementary Table S8). In addition to PANSS sub-scores, disorganization and impoverishment factor scores were calculated. Given that participants were receiving anti-psychotic treatment, these scores were low (mean disorganization factor: 1.48, SD = 0.49; mean impoverishment score: 1.28, SD = 0.64). Spearman’s correlation analysis revealed no significant relationship between the urbanicity index and either the disorganization factor (r = –0.12; p = .32) or the impoverishment scores (r = –0.16; p = .22). However, a significant negative correlation was observed between GI and duration of illness in several brain regions: left posterior cingulate cortex (p < .001), left lateral orbitofrontal cortex (p < .001), left superior frontal cortex (p < .02), right paracentral gyrus (p < .001), right pars opercularis (p < .001) and right insula (p = .005) (details in Supplementary Table S9). We did not find any notable associations between GI and either age of onset or olanzapine-equivalent anti-psychotic dosages. Regarding clinical severity, several correlations were noted between the GI and PANSS scores: (a) a significant positive correlation with PANSS-positive score and GI in the right superior parietal gyrus (BA7; p < .001); (b) a significant negative correlation with PANSS-negative score in the left supramarginal gyrus (p < .001), right supramarginal gyrus (p = .002), right lateral occipital gyrus (p < .001), and right inferior temporal gyrus (p < .001); (c) a significant positive correlation with PANSS-total score in left and right superior frontal gyri (p < .001), left precuneus (p < .001), and right precuneus (p = .022).

Discussion

This is the first study in the published literature to investigate the relationship between urban upbringing and cortical gyrification in individuals with SCZ. Our findings show that both urban birth and upbringing are associated with increased gyrification in several cortical regions. In patients with SCZ, increased gyrification was primarily observed in the frontal lobes, whereas in HCs, it was present both in the frontal and occipital lobes. Furthermore, the effect of urban birth on gyrification was significant only in the SCZ group, indicating that urban birth and upbringing may differentially influence cortical development in SCZ.

Cortical gyrification is a carefully regulated developmental process occurring during gestation and early childhood.⁵⁵ The GI peaks in infancy and gradually declines through adolescence and early adulthood.²⁸ It is shaped by genetically driven neuronal migration and differentiation, as well as mechanical tension from intracortical axons connecting various brain regions.^41,55,56 While genetics plays a major role, growing evidence highlights the significant impact of environmental factors on cortical gyrification.²⁷ Adverse prenatal conditions—such as intrauterine growth restriction, hypoxia, preterm birth, prenatal alcohol exposure, and viral infections—affect gyrification.²⁷ The observation that monozygotic twins do not show greater similarity in GI than dizygotic twins underscores the importance of perinatal environmental influences.⁵⁷ Additionally, factors such as intense physical training and early-life socio-economic status also significantly impact GI.^{58, 59} Taken together, our results support the idea that environmental factors during early development play a crucial role in shaping cortical gyrification.²⁴

Our study’s key finding is that urban birth and upbringing are associated with increased cortical gyrification in SCZ. Previous research has reported increased gyrification in first-episode SCZ patients, individuals at high risk for psychosis, and siblings of patients, with increased gyrification also predicting transition to psychosis in high-risk groups.^41,60–64 These findings suggest that adverse environmental exposures, like urban upbringing, may alter neurodevelopmental trajectories in SCZ. Urban ecological factors such as maternal viral infection, social stress, environmental toxins, and higher maternal substance use are known risk factors for SCZ and have been linked to abnormal gyrification.²⁷ A recent study also found associations between residential air and noise pollution, local GI, and neurocognitive performance.⁶⁵ While our results indicate a link between urbanicity and cortical gyrification, urban living can also induce other neurobiological changes, including alterations in cortical volume and thickness. Since urbanicity encompasses multiple factors,^5,6,66 future research should dissect the relative contributions of these elements.

Several studies have investigated the relationship between urban upbringing and cortical thickness or volume.^8,9,14,15,67 However, the association with cortical gyrification remains understudied. Only one prior study in healthy individuals reported a positive correlation between urbanicity and gyrification scores, consistent with our findings.⁸ Although epidemiological evidence links urban upbringing to an increased risk of SCZ,^{5, 66} no prior research has directly investigated the relationship between cortical gyrification and urban birth or upbringing in SCZ. A study examining grey matter volume in psychotic disorders found that cannabis use and developmental urbanicity were associated with reduced grey matter.¹⁵ In contrast, another study by the same group reported no association between cortical thickness and developmental urbanicity.¹⁴ Given the different developmental trajectories of cortical thickness and gyrification,⁶⁸ these measures cannot be directly compared.

The link between urban upbringing and an increased risk of SCZ is well established, but causality remains uncertain. Residential choices are influenced by socio-economic status, lifestyle, genetics, and personality traits, which may confound this relationship. Studies have linked higher polygenic risk scores for SCZ to a greater likelihood of living in urban areas, suggesting genetic predisposition may influence residential patterns.^{69, 70} Since children typically do not choose their living environment, urban upbringing reflects both within- and across-generation factors. A recent study found that individuals with higher genetic risk for SCZ were more likely to experience socio-environmental risks like urban upbringing.

Nevertheless, the association between such risks and psychotic experiences remained even after accounting for gene-environment correlation.⁷¹ This highlights the complex interplay of genetic and environmental factors in the development of SCZ. Our study did not investigate genetic or other environmental risks, but it did find a significant link between urban upbringing and altered cortical gyrification. However, these brain structure differences should be viewed as correlational rather than causal and cannot be attributed solely to urban living.

Compared to HCs, SCZ patients exhibited reduced gyrification, primarily in the frontal and occipital regions, consistent with prior research.^72–75 Yet, some studies report increased gyrification in SCZ, particularly in the frontal cortex.^61,62,76,77 These discrepancies may reflect illness duration; we observed a negative correlation between illness length and GI. Previous studies similarly found decreased gyrification in chronic SCZ but increased gyrification in first-episode patients.⁴¹ A longitudinal study also documented a progressive decline in gyrification over two years in SCZ.⁷⁸

Another possible reason for variability across studies is the use of different methods and software to calculate GI. Early studies relied on manual tracing of the pial and outer smoothed surfaces in 2D MRI slices, a method that is time-consuming, less accurate, difficult to reproduce, and rater-dependent.⁶¹ Automated GI (A-GI) methods were later developed to overcome these limitations by automatically tracing surfaces³⁸; however, A-GI accuracy can still be affected by slice orientation and cannot detect hidden sulci. The local GI (LGI) method implemented in FreeSurfer utilizes surface-based morphometry, reconstructing 2D MRI data into detailed 3D models (~300,000 vertices).^{79, 80} At each vertex, LGI is computed as the ratio of the pial surface to the outer smoothed surface within a spherical region of interest, allowing precise whole-brain and regional analyses with improved accuracy and reproducibility. We used FreeSurfer for LGI calculations in this study,³⁸ but other studies have used different software, and differences between packages are known to affect neuroimaging results.⁸¹ Future work should systematically examine how software choice influences gyrification measures.

Another important methodological consideration is the effect of anti-psychotic medication. Several studies have shown that anti-psychotics influence cortical thickness and volume, though findings are inconsistent, likely due to differences between first- and second-generation drugs and treatment duration.^82–84 Generally, short-term treatment may increase brain volume. At the same time, long-term use is associated with volume reductions, particularly in the frontal and temporal regions, with higher doses and longer treatment durations correlating with greater decreases in volume.^85–87 A meta-analysis also associated cortical thickness alterations with anti-psychotic use.⁸⁸ However, the impact of anti-psychotics on cortical gyrification remains underexplored. A recent meta-analysis suggested that gyrification abnormalities in first-episode SCZ reflect abnormal neurodevelopment, whereas cortical thickness reductions in chronic SCZ may be due to anti-psychotic effects.⁸⁹ In our study, all patients were receiving anti-psychotics. We found no association between anti-psychotic dosage and GI, but we only assessed dosage cross-sectionally and could not calculate cumulative exposure due to the chronic nature of illness. Therefore, we cannot exclude anti-psychotic effects as a confounder. Future research should include first-episode SCZ patients to isolate illness-related changes from medication effects better and clarify the impact of chronicity on gyrification.

Limitations

First, the groups were not age-matched, and previous research has documented age-related changes in cortical gyrification.³² Although age was included as a covariate, we cannot completely rule out its confounding effects. Future studies should strive for closer age matching to minimize this influence. We assessed the urbanicity index during the first 15 years of life, a period when tertiary gyrification typically completes; however, it remains unclear whether earlier versus later developmental stages have distinct impacts on cortical maturation. Future research should explore these temporal effects. Second, birthplace and residency duration data were collected retrospectively and may be subject to recall bias. However, moving is a major life event, reducing the likelihood of significant errors. Prospective studies or those with detailed migration histories could better address this limitation. Third, our sample included only SCZ patients, excluding other non-affective psychoses. While these disorders share etiological and neurobiological features, focusing on SCZ ensured sample homogeneity. Nonetheless, SCZ itself is heterogeneous, and larger, more diverse samples are needed to validate and extend our findings to broader clinical populations. Fourth, we were unable to control for socio-economic status during childhood due to a lack of reliable historical data, despite its known influence on neurodevelopment. Future research should match participants on socio- economic status or include it as a covariate. Additionally, census data used to define urbanicity were collected every ten years, during which significant socio-economic changes may have occurred, especially in semi-urban and urban areas. Our study was unable to account for these temporal variations. Fifth, we lacked measures of premorbid IQ and cognitive function, which limited our ability to assess their potential confounding effects. Future studies should incorporate validated cognitive assessments. Finally, due to the absence of reliable antenatal and perinatal records, we were unable to control for other early-life factors that may affect brain development. Future investigations should include a wider range of variables—such as adverse childhood experiences, nutritional status, current residence, and other environmental factors—in multivariate analyses to better understand their contributions to neurodevelopmental outcomes.⁹⁰

Conclusions

Our findings suggest that both birthplace and the upbringing environment influence neurodevelopment. While specific risk factors remain unidentified, future longitudinal studies should explore the complex interplay of environmental and genetic influences. As rapid urban migration continues in developing countries, understanding the impact of urban living on brain development is essential. Identifying these risk factors and their mechanisms could inform preventive strategies and guide the design of healthier urban environments.

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Supplemental material for this article available online.

Supplemental Material

Supplemental material for this article available online.

Footnotes

Data Availability Statement

Data will be available on request from the authors.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Declaration Regarding the Use of Generative AI

AI was not used in manuscript preparation.

Ethics Approval and Informed Consent Statements

Ethics approval was obtained from the institutional ethics committee for all three studies (approval numbers: study 1 NIMHANS/85th IEC/2013, dated May 17, 2013; study 2 NIMHANS/105th IEC/2016, dated July 2, 2016; study 3 NIMHANS/105th IEC/2016, dated July 2, 2016). These studies adhered to the principles outlined in the Declaration of Helsinki. All the study participants provided informed consent, which the Institutional Ethics Committee approved.

Funding

The authors disclosed receipt of the following financial support for the research, authorship and/or publication of this article: Department of Science and Technology, Government of India (PI: Dr. Naren P Rao; IFA/12/LSBM/36; SR/CSRI/138/2015; SR/SATYAM/304/2015). The funding agency had no role in the interpretation of data or manuscript preparation.

Prior Presentations

The authors declare that the present study findings were not presented at any national or international conference.

Simultaneous Submission to Another Journal or Resource

The authors declare that the present study manuscript has not been submitted to any other journal or resource. We are submitting it to the Indian Journal of Psychological Medicine first time.

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