You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


1. Introduction {#sec1}
===============

It is well established that early-blind adults, i.e., those individuals who lose sight at birth or within a short period after birth, show alterations in the neural structure and function due to the absence of visual inputs. Structurally, the morphological changes of the grey matter (GM) in early-blind adults have been studied extensively and include decreased GM volume in the primary visual area \[[@B1]--[@B5]\]; increased GM volume in the occipital, frontal, and entorhinal cortices \[[@B4]\] and the globus pallidus \[[@B6]\]; and increased cortical thickness of the early visual areas \[[@B2], [@B7], [@B8]\]. In addition to GM alterations, white matter (WM) alterations were also identified based on the diffusion tensor imaging (DTI) datasets of early-blind adults. In detail, microstructural alterations were mainly found in the optic radiation and the lateral geniculate nucleus with decreased WM volume \[[@B2], [@B9], [@B10]\] or density \[[@B9], [@B11]\]. Other microstructural disruptions due to early visual deprivation were also found in the inferior longitudinal fasciculus \[[@B12]\] and the occipital/temporal/parietal thalamocortical projections \[[@B4], [@B13]\]. In addition, Leporé et al. \[[@B3]\], using tensor-based morphometry, reported interesting hypertrophy in the prefrontal and parietal WM and in a section of the splenium of the corpus callosum in early-blind adults. Interestingly, some studies \[[@B11], [@B14]\] also identified increased fractional anisotropy in the corticospinal tract in early-blind adults by tract-based quantitative analysis, suggesting structural neuroplasticity. To some extent, such morphological changes reflect deprivation-induced processes, both neurodegenerative and neurodevelopmental, following the loss of visual input and/or altered visual experience \[[@B3], [@B5]\].

In addition to structural alterations, functional changes were also identified in early-blind adults in many studies. Task-based research using functional magnetic resonance imaging (fMRI) demonstrated that the occipital cortex could process nonvisual information, such as tactile \[[@B15], [@B16]\] and auditory input \[[@B16]--[@B20]\], and some complex cognitive- and verbal-related input \[[@B17], [@B21]--[@B23]\]. These results collectively suggested that the visual cortex of blind individuals exhibits cross-modal reorganization properties. Moreover, activity in the primary, secondary, and tertiary visual cortices of blind adults was increased, spatially and in amplitude, after sensory substitution training, even in short-term (10 min) \[[@B24]\]. Using resting-state fMRI (rs-fMRI), both decreased functional connectivity \[[@B7], [@B22], [@B25]--[@B28]\] and increased functional connectivity of the occipital cortex \[[@B25], [@B29], [@B30]\] were identified in early-blind adults compared with sighted controls, providing extra evidence of functional neuroplasticity in early-blind adults.

However, these results about structural and functional disruptions and neuroplasticity were derived from early-blind adults. Recently, we investigated resting-state interhemispheric functional connectivity in early-blind adolescents (EBAs) and found significantly lower voxel-mirrored homotopic connectivity (VMHC) in the primary visual cortex, visual association cortex, and somatosensory association cortex \[[@B31]\]. Although the study focused on EBAs, it is still unclear if this group would show structural and functional alteration patterns similar to those of early-blind adults. Moreover, most of the previous studies on early blindness included subjects with residual light perception, thus failing to exclude possible effects of residual light perception on the structure and function of the blind brain. Therefore, we aimed to (1) investigate whether and how the brain\'s structure and function are altered in EBAs and (2) investigate the effects of residual light perception on brain structure and function. Given the evidence just discussed, we hypothesized that structural and functional alterations in EBAs would mostly be identified in the brain regions associated with vision. To verify this hypothesis, we recruited a cohort of 23 EBAs (8 with residual light perception (LPs), 15 without light perception (NLPs)) and 21 NSCs (age range: 11--19 years old) to explore the structural and functional brain changes in EBAs using the whole-brain voxel-based analysis (VBA) and the resting-state functional connectivity (RSFC).

2. Materials and Methods {#sec2}
========================

2.1. Subjects {#sec2.1}
-------------

Twenty-three EBAs with onset age \< 1 year were enrolled from the Guangzhou City Blind School. Among them, 8 EBAs with residual light perception were included in the LP group (6 males and 2 females) and 15 EBAs without light perception were included in the NLP group (8 males and 7 females). Twenty-one age- and sex-matched normal-sighted volunteers were recruited as the NSC group (10 males and 11 females). The demographics of the three groups are summarized in [Table 1](#tab1){ref-type="table"}. All subjects met the following inclusion criteria: (1) right-handed and (2) age ranging from 11 to 19 years. The exclusion criteria were (1) any history of psychiatric or neurologic diseases, (2) symptomatic or atypical neuralgia, and (3) identifiable MRI abnormalities, such as demyelination, vascular malformations, or tumors. All individuals and their guardians signed a written informed consent form prior to the MRI examinations. All research procedures were approved by the Ethics Committee of the First Affiliated Hospital of Jinan University.

2.2. Data Acquisition {#sec2.2}
---------------------

A 3.0T MRI scanner (Discovery MR750 System; General Electric, Milwaukee, WI, USA) with an 8-channel head coil was used. The subjects were scanned in the supine position and were placed head first into the scanner. The position of the head was fixed using several foam cushions. For each subject, the DTI and rs-fMRI data were acquired. For DTI, we acquired 75 diffusion-weighted images (*b* = 1000 s/mm^2^) and 5 nondiffusion-weighted images (*b* = 0 s/mm^2^) using a spin-echo echo-planar imaging sequence with the following parameters: number of excitations = 1, repetition time (TR) = 6000 ms, echo time (TE) = 68 ms, acquisition matrix = 128 × 128, and voxel size = 2 mm × 2 mm × 3 mm. The array spatial sensitivity encoding technique was used with an acceleration factor of 2 to reduce acquisition time and anamorphosis. The rs-fMRI data were acquired using an echo-planar imaging pulse sequence, sensitive to blood-oxygen-level-dependent contrast (TR = 2000 ms, TE = 35 ms, flip angle = 90°, matrix size = 64 × 64, field of view = 256 × 256 mm^2^, slice thickness = 3 mm, gap = 0.6 mm, 240 time points, sequence length = 8 min, and 41 slices in the axial plane). During rs-fMRI, subjects were instructed to stay awake, not to move, to think of nothing in particular, to relax, and to keep their eyes closed.

2.3. VBA and Statistics Analysis {#sec2.3}
--------------------------------

We performed DTI preprocessing using the FMRIB Software Library (FSL5.0; <http://www.fmrib.ox.ac.uk/fsl>), including eddy-current and motion correction, brain mask creation for the DTI data, exclusion of voxels outside the braincase, and diffusion tensor reconstruction with the "DTIFIT" toolbox \[[@B10]\]. All diffusion-related parameters including fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) were calculated. Following this, individual FA maps were analyzed using the statistical parametric mapping software (SPM8; Wellcome Department of Cognitive Neurology, London, UK; <http://www.fil.ion.ucl.ac.uk/spm>). The FA maps were normalized to the standard Montreal Neurological Institute (MNI) template and smoothed with a 6 mm full-width at half-maximum (FWHM) Gaussian kernel to reduce the effects of misregistration in spatial normalization \[[@B32]\]. Two-sample *t*-tests were conducted to assess intergroup differences in FA between the groups with age and sex as covariates. In order to account for multiple comparisons, the AlphaSim method was used with a voxel-level threshold of *p* \< 0.001 and a cluster-level threshold of *p* \< 0.05. VBA of the MD, RD, and AD maps was performed similarly.

The clusters showing significant differences in the DTI parameters between the groups were identified according to the standard brain atlas, and the DTI values from these significant clusters of the corresponding tensor parameter maps (FA, MD, RD, and AD) in the subjects were extracted. Subsequently, correlation analysis followed by an independent two-sample *t*-test was performed using the Statistical Package for the Social Sciences (SPSS) software (version 23.0; IBM Corporation, NY, USA). For correlations between DTI metric values and age, the Pearson correlation coefficient was computed separately for each group. Two-sample *t*-tests were used to detect differences in the DTI values between the groups.

2.4. Resting-State fMRI Data Preprocessing {#sec2.4}
------------------------------------------

The rs-fMRI data were preprocessed using the toolbox for Data Processing & Analysis for Brain Imaging (DPABI, <http://rfmri.org/dpabi>) \[[@B33]\]. The preprocessing steps were (1) discarding the first 10 time points to avoid transient signal changes that occurred before magnetization reached the steady state and subjects\' adaptation to the scanning noise; (2) slice timing correction; (3) head motion realignment; (4) normalization to the standard MNI template in the DPABI package; (5) smoothing with a 6 mm Gaussian kernel; (6) linear regression to remove confounding factors, including six head motion parameters, as well as the ventricle, WM, and global signals; (7) scrubbing with interpolation to remove volumes with head motion spatial shift larger than 1.5 mm and rotation greater than 1.5° in any direction; and (8) band-pass filtering from 0.01 to 0.1 Hz. The resulting images were manually inspected by two experienced researchers for image quality.

2.5. RSFC and Statistics Analysis {#sec2.5}
---------------------------------

To investigate the corresponding functional alterations, the clusters showing significant group difference in VBA were selected as the seed ROIs for RSFC analysis. Firstly, the peak coordinates of each significant cluster obtained from VBA were determined and used to create a ROI with a radius of 6 mm. Secondly, the RSFC was examined with a seed-voxel correlation approach, in which the correlations of the mean time-course signal in a seed region with all other voxels in the whole brain were calculated. Before statistical analysis, the correlation coefficients were transformed into *z* values using the Fisher *r*-to-*z* transformation to improve the normality. Two-sample *t*-tests between the groups were then performed using the DPABI toolbox, with age, sex, and head motion parameters as covariates. The statistical results were corrected for multiple comparisons using the AlphaSim method, with a voxel-level threshold of *p* \< 0.001 and a cluster-level threshold of *p* \< 0.05. Finally, the results were projected onto a smoothed International Consortium for Brain Mapping (ICBM152) surface template using the BrainNet Viewer (<http://www.nitrc.org/projects/bnv/>) \[[@B34]\].

3. Results {#sec3}
==========

3.1. Demographic Characteristics {#sec3.1}
--------------------------------

Six male and two female subjects were included in the LP group and eight male and seven female subjects in the NLP group; we also recruited 21 age- and sex-matched NSCs. Analysis of variance (ANOVA) followed by two-sample *t*-tests and chi-square tests were performed to detect age and sex differences, respectively, between the various groups ([Table 2](#tab2){ref-type="table"}). No significant differences in the demographic characteristics were identified between the three groups (age: *t* = −1.657, *p* = 0.112 comparing the LP and NLP groups; *t* = −1.883, *p* = 0.071, LP vs. NSC; *t* = 0.271, *p* = 0.788, NLP vs. NSC; sex: *p* = 0.400, LP vs. NLP; *p* = 0.238, LP vs. NSC; and *p* = 1.000, NLP vs. NSC).

3.2. VBA Results {#sec3.2}
----------------

The two-sample *t*-test revealed a reduced FA in the left occipital lobe/subgyral, right parahippocampal gyrus, and right superior occipital gyrus in NLPs compared to NSCs. We also found higher MD in the bilateral lingual gyrus, as well as higher RD in the right lingual gyrus and right parahippocampal gyrus in NLPs, compared with NSCs (*p* \< 0.001, AlphaSim corrected; [Table 3](#tab3){ref-type="table"} and [Figure 1](#fig1){ref-type="fig"}). No significant AD alterations were observed between these two groups. Compared with NSCs, LPs showed significant FA reduction in the bilateral middle occipital gyrus, left lingual gyrus, left cuneus, left parahippocampal gyrus, and right parietal lobe/subgyral (*p* \< 0.001, AlphaSim corrected; [Table 3](#tab3){ref-type="table"} and [Figure 2](#fig2){ref-type="fig"}). No significant differences in MD, RD, and AD were found when comparing the LP and NSC groups. No elevated FA or reduced diffusivity parameters were found for any of the clusters in NLPs or LPs compared with NSCs, even with a loose threshold (*p* \< 0.01, AlphaSim corrected). Unfortunately, the comparison of all diffusion tensor maps between the LP and NLP groups showed no results at the voxel level, at AlphaSim-corrected *p* \< 0.001. However, some voxels in the brain of the LP group exhibited trends (at a voxel-level AlphaSim-corrected *p* \< 0.01) towards increased/decreased DTI parameters compared with NLPs, including reduced FA in the right fusiform gyrus, right middle temporal gyrus, and superior occipital gyrus; increased FA in the right superior frontal gyrus; increased MD, RD, and AD in the right fusiform gyrus and middle temporal gyrus; and reduced MD, RD, and AD in the bilateral caudate ([Table SI](#supplementary-material-1){ref-type="supplementary-material"} and [Figure SI](#supplementary-material-1){ref-type="supplementary-material"} in the Supplementary Materials).

3.3. Correlation Analysis Results {#sec3.3}
---------------------------------

Correlation analysis showed positive and significant correlations between the MD and RD values of the right lingual gyrus and age in NLPs (MD: *r* = 0.601, *p* = 0.018; RD: *r* = 0.536, *p* = 0.040), but negative and significant correlations in NSCs (MD: *r* = −0.488, *p* = 0.025; RD: *r* = −0.508, *p* = 0.019). In NSCs, the FA values of the left occipital lobe/subgyral (*r* = 0.538, *p* = 0.012) and the right superior occipital gyrus (*r* = 0.560, *p* = 0.008) were positively correlated with age, while the MD values of the left lingual gyrus were negatively correlated with age (*r* = −0.508, *p* = 0.019) ([Figure 3](#fig3){ref-type="fig"}).

3.4. Head Motion Effects {#sec3.4}
------------------------

Head motion time courses were computed by estimating the shift in each direction and the rotations about each axis for each of the 230 consecutive volumes. To assess the head motion confounders, we calculated the mean framewise displacement among the three groups (LP: 0.35 ± 0.23 mm; NLP: 0.22 ± 0.17 mm; and NSC: 0.26 ± 0.27 mm). Moreover, ANOVA was used to identify potential differences in head motion between the 3 groups, but none was found. (ANOVA: *F* = 0.845, *p* = 0.437).

3.5. RSFC Results {#sec3.5}
-----------------

The whole-brain RSFC of the brain regions with significant microstructural alterations was analyzed and compared between the NLP and NSC groups. The left occipital lobe/subgyral of NLPs showed enhanced RSFC with the left inferior frontal triangular areas, left middle frontal gyrus, and right inferior frontal opercular areas; the right parahippocampal gyrus of NLPs showed enhanced RSFC with the left superior frontal gyrus; the right superior occipital gyrus of NLPs showed enhanced RSFC with the right supramarginal gyrus/left superior frontal gyrus and decreased RSFC with the left fusiform gyrus (*p* \< 0.001, AlphaSim corrected; [Table 4](#tab4){ref-type="table"} and [Figure 4](#fig4){ref-type="fig"}). When comparing LPs with NSCs, only the left middle occipital gyrus showed a significantly decreased RSFC in LPs with the right middle occipital gyrus, when setting the bilateral middle occipital gyrus, left parahippocampal gyrus, left lingual gyrus, left cuneus, and right parietal lobe/subgyral as ROI seeds (*p* \< 0.001, AlphaSim corrected; [Table 4](#tab4){ref-type="table"}).

To investigate the effects of residual light perception on the brain function in blind brains, the differences in the whole-brain RSFC of the brain regions showing microstructural alterations between the LP and NLP groups were analyzed and compared. A trend of RSFC change in LPs relative to NLPs could also be observed and specifically enhanced RSFC between the right fusiform gyrus and the right superior frontal orbital areas/right middle temporal gyrus and between the right superior frontal gyrus and the left inferior temporal gyrus. On the contrary, the RSFC between the right superior frontal gyrus and the right postcentral gyrus/superior frontal gyrus was decreased in the LP compared to the NLP group (*p* \< 0.01, AlphaSim-corrected; [Table SII](#supplementary-material-1){ref-type="supplementary-material"} and [Figure SII](#supplementary-material-1){ref-type="supplementary-material"} in the Supplementary Materials).

4. Discussion {#sec4}
=============

We investigated the alterations of brain microstructure and their corresponding functional connectivity in EBAs with and without light perception using the DTI and rs-fMRI datasets. Compared to NSCs, both EBA groups showed significant brain microstructural disruptions mainly in the posterior visual pathway of the occipital lobe and the extrastriate visual cortex. Moreover, the correlations between bilateral occipital cortex FA or bilateral lingual gyrus MD and age were consistent with the developmental trajectory of the brain in NSCs, but inconsistent or even opposite in EBAs. Additionally, using brain regions with significant DTI parameter changes in our study as the ROIs, we identified enhanced RSFC in the occipital cortex with the bilateral frontal cortex and the homolateral parietal cortex and reduced intrahemispheric RSFC in the right visual cortex in NLPs, as well as reduced interhemispheric RSFC within the middle occipital cortex in LPs compared with NSCs. Our findings provide additional evidence that early visual deprivation may lead to functional neuroplasticity earlier than structural neuroplasticity in EBAs.

4.1. Disruptions in the Brain Microstructure of EBAs {#sec4.1}
----------------------------------------------------

The FA reduction in the bilateral visual pathway of the occipital cortex in both EBA groups, elevated MD of the bilateral lingual gyrus, and elevated RD of the right lingual gyrus in NLPs vs. NSCs were consistent with the findings of previous research on young or middle-aged blind adults \[[@B2], [@B9], [@B12], [@B35], [@B36]\]. These structural disruptions were also supported by other evidence of reduced anatomical connectivity associated with the visual cortex \[[@B12], [@B37]\] and decreased GM and WM volume in the occipital cortex of blind individuals \[[@B6], [@B38]\]. Our study also validated the GM and WM changes in the visual cortex of EBAs at the microstructural level, which can be explained by transneuronal degeneration and/or immaturity due to early visual deprivation \[[@B9], [@B39]\]. Moreover, we found diminished FA in the left parahippocampal gyrus of LPs and the right parahippocampal gyrus of NLPs compared to NSCs. The right parahippocampal gyrus is important for learning and visuospatial configuration of objects \[[@B40]\], while the left parahippocampal gyrus is involved in cumulative verbal memory \[[@B41]\]. Considering the asymmetry function of the bilateral parahippocampal gyri during learning, their inconsistent alteration in the two groups may provide evidence of different learning patterns between the LP and NLP groups.

Furthermore, to investigate the effects of residual light perception on brain microstructure, we compared the DTI parameters between LPs and NLPs. The results showed increased FA in the right superior frontal gyrus and reduced diffusivity parameters in the bilateral caudate nucleus in LPs compared with NLPs. Since the superior frontal gyrus is anatomically connected with the cognitive control network and functionally involved in complex cognitive processing \[[@B42], [@B43]\], the increased FA of the right superior frontal gyrus in LPs vs. NLPs may suggest that residual light perception could play a role in complex cognitive function or reduce the damage to cognitive function induced in EBAs by the lack of visual information. As the caudate nucleus is known to integrate spatial information with motor behavior and is associated with some learning processing \[[@B44]--[@B46]\], its decreased diffusivity parameters in LPs vs. NLPs demonstrate that the function of guiding motor performance and learning in the caudate nucleus may be partly mediated by light perception.

We also calculated the correlations between age and DTI metrics of the altered brain regions in the three groups. The FA of the bilateral occipital cortex was positively correlated with age in NSCs but not in NLPs, and the MD and RD of the right lingual gyrus were negatively correlated with age in NSCs, but positively in NLPs. The trends of DTI parameters alteration with age in NSCs were consistent with the developmental trajectory of WM in the young \[[@B47]--[@B49]\], while these trends were not observed or were reversed in NLPs. This inconsistency suggests that microstructural disruptions caused by early visual deprivation in the corresponding brain regions may progressively accumulate in adolescents. Given the continuous development of WM in adolescence, a probably more reasonable explanation for the trend shown by the NLPs is the coexistence of blindness-induced damage and normal cerebral development.

4.2. Alterations in Functional Connectivity {#sec4.2}
-------------------------------------------

Changes in brain microstructure preferentially point to abnormalities in brain functional connectivity \[[@B50], [@B51]\]. Resting-state fMRI can reflect the integrated features of intrinsic functional connectivity in the brains of visually impaired persons \[[@B27]\]. Many previous studies reported changes in RSFC between the occipital cortex and other sensory \[[@B7], [@B25]--[@B27], [@B52]\], somatosensory \[[@B25], [@B26], [@B28], [@B52], [@B53]\], and motor cortices \[[@B25], [@B26], [@B28]\] in early-blind adults. In the current study, the occipital gyrus in NLPs showed enhanced RSFC with some frontal areas, including the left inferior frontal triangle, left middle frontal gyrus, left superior frontal gyrus, and right inferior frontal opercular areas. Such findings are consistent with those of previous studies \[[@B54], [@B55]\]. In healthy humans, these frontal regions are classically considered as high-level areas related to language and cognition and involved in verbal fluency \[[@B56], [@B57]\], single word and motor speech production \[[@B58], [@B59]\], and error detection and imitation \[[@B60], [@B61]\]. Beyond these classical functions, they also participate in verbal working memory \[[@B62], [@B63]\]. In blind individuals, the occipital visual cortex and the frontal language and cognitive areas were activated when performing Braille reading tasks \[[@B64], [@B65]\]. These findings indicated that these frontal areas might play an important role, related to the language network, in the blind brain. Additionally, compared with sighted controls, early-blind participants had more extensive activity in the occipital visual areas and frontal language areas when performing a vibrotactile memory task \[[@B64], [@B65]\]. All evidence may explain why the RSFC between the occipital areas and frontal areas was increased in the NLPs. From another perspective, the elevated RSFC between the occipital visual areas and the frontal language regions can provide compelling evidence for the existence of functional neuroplasticity in NLPs.

We also observed reduced interhemispheric RSFC between the bilateral middle occipital gyrus in LPs and between the right superior occipital gyrus and the left fusiform gyrus in NLPs compared with NSCs, extending the findings of many previous studies \[[@B22], [@B25], [@B31], [@B66]\]. According to some published reports, such reduced interhemispheric communication may be interpreted as extensive abnormalities in WM integrity, particularly in the corpus callosum \[[@B67], [@B68]\]. Many existing studies found WM abnormalities in the splenium of the corpus callosum, thought to play a crucial role in the communication between the two cerebral hemispheres, in early-blind young, middle-aged, or elderly adults \[[@B3], [@B10], [@B35], [@B69], [@B70]\]. However, no WM structural abnormalities were found in the splenium of the corpus callosum in the present study. One possible reason may be that the posterior part of the corpus callosum continues to develop during adolescence and increases in density \[[@B47], [@B48], [@B71], [@B72]\], likely counteracting the neurodegenerative effects of visual deprivation \[[@B73]\]. Based on prior and present findings, we speculated that the RSFC changes in EBAs might result not only from a combination mechanism of general loss and complementary plasticity \[[@B25]\] but also from an antagonistic mechanism of neural development. Thus, our findings provide new information regarding the functional profiles that emerge in the early life of blind adolescents.

Furthermore, we evaluated the RSFC differences between the LP and NLP groups to investigate the differences in functional connectivity patterns depending on residual light perception. Compared with NLPs, LPs showed enhanced RSFC in the right fusiform with the homolateral middle temporal gyrus and reduced RSFC in the right superior frontal gyrus with the right postcentral gyrus. In sighted subjects, the fusiform and middle temporal gyri have been reported to be involved in the ventral and dorsal visual streams, respectively, and to be robustly activated during object shape/location detection and visual-motor tasks \[[@B74], [@B75]\]. There is no doubt that light perception is crucial for such processing. Therefore, the residual perception of light in LPs may account for the enhanced RSFC between these regions. Given that the postcentral gyrus is well-known as the primary somatosensory area and is associated with fine touch sense, the decreased RSFC of this area in LPs may suggest that the touch sense of early-blind individuals with residual light perception is not as strong as that of those without light perception.

4.3. Limitation {#sec4.3}
---------------

Our findings of the effects of residual light perception on brain microstructure and function in EBAs were derived at a loose level of statistical significance, presumably due to the small sample size. Therefore, these results should be interpreted with caution, and further studies with larger sample size will be needed to provide solid evidence of these phenomena.

5. Conclusion {#sec5}
=============

The present study demonstrated significant microstructural and functional alterations in EBAs with and without residual light perception when compared to NSCs. Our findings provide additional evidence that early visual deprivation may lead to functional neuroplasticity earlier than structural neuroplasticity in EBAs. The structural alterations in EBAs contain complex mechanisms including general loss, complementary plasticity, and neural development. Collectively, these results provided new insights into the mechanisms underlying the reorganization of the brain in adolescents with early visual deprivation.

This work was supported by the Shenzhen Sanming Project (SZSM201512038), the Science and Technology Innovation Program of Shenzhen (JCYJ20160531184531506), and the Natural Science Foundation of Guangdong Province (Nos. 2017A030313744 and 2016A030313180).

Data Availability
=================

The datasets generated and/or analyzed during the current study are available from the corresponding author Dr. Hengguo Li (<<PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>>) on reasonable request.

Conflicts of Interest
=====================

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Authors\' Contributions
=======================

ZZ, XL, FH, JZ, and JL acquired and analyzed data. JX, XL, and HL conceived this study and designed the experiments. ZZ wrote the article with the help of JX, LS, and QH. All authors were involved in data interpretation and critically revising the manuscript.

Supplementary Materials {#supplementary-material-1}
=======================

###### 

Table SI: brain regions with significant changes in diffusion tensor imaging metrics between LPs and NLPs. Table SII: brain regions with significant changes of RSFC between the LP and NLP groups. Figure SI: group differences of fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) using voxel-based analyses in LPs compared to NLPs (AlphaSim corrected with a voxel-level threshold of *p* \< 0.01 and a cluster-level threshold of *p* \< 0.05). Blue and red regions denote decreased and increased DTI parameters, respectively. The numbers at the top indicate the *z* value of MNI coordinates. Figure SII: brain regions that showed altered functional connectivity with FG.R (A) and SFG.R (B), respectively, in LPs compared to NLPs. Two sample *t*-tests were performed to explore the intergroup differences. The results were corrected by the AlphaSim method at a voxel level of *p* \< 0.01. All abbreviations of the brain regions are shown in Tables SI and SII.

###### 

Click here for additional data file.

![Group differences of fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) in NLPs compared to NSCs (*p* \< 0.0001, AlphaSim corrected). Blue regions denote lower FA values and red regions denote higher diffusivity values. The numbers at the top indicate the *z* value of MNI coordinates.](NP2019-2747460.001){#fig1}

![Group differences of fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) in LPs compared to NSCs (*p* \< 0.001, AlphaSim corrected). Blue and red regions denote lower and higher DTI parameters, respectively. The numbers at the top indicate the *z* value of MNI coordinates.](NP2019-2747460.002){#fig2}

![Pearson\'s correlation analyses between age and DTI metrics of OG.L, SOG.R, LING.R, and LING.L, respectively, in the NLP and NSC groups. In the scatter plots, the DTI metrics of each significant region are displayed for each subject in the specific groups, with dots for NLPs shown in violet and NSCs in green. The correlation coefficients and *p* values are next to each scatter plot. All abbreviations of brain regions are shown in [Table 3](#tab3){ref-type="table"}.](NP2019-2747460.003){#fig3}

![Brain regions that showed altered functional connectivity with OG.L (a), ParaHIP.R (b), and SOG.R (c), respectively, in NLPs compared to NSCs. Two-sample *t*-tests were performed to explore the between-group differences. The results were corrected by the AlphaSim method at a voxel level of *p* \< 0.001. All abbreviations of the brain regions are shown in Tables [3](#tab3){ref-type="table"} and [4](#tab4){ref-type="table"}.](NP2019-2747460.004){#fig4}

###### 

Clinical characteristics of early-blind adolescents and normal-sighted controls.

  No.     Age (years)   Gender   Age of onset (months)   Causes of blindness
  ------- ------------- -------- ----------------------- ---------------------
  LP01    11.8          M        2                       ROP
  LP02    14.7          M        2                       Cataract
  LP03    14.4          M        0                       CG; EA
  LP04    11.0          M        0                       CRL
  LP05    12.3          M        3                       ROP
  LP06    12.5          F        8                       ROP
  LP07    13.8          M        0                       CRL
  LP08    15.0          F        0                       CRL
  NLP01   14.3          M        3                       ROP
  NLP02   16.3          M        2                       ROP
  NLP03   17.2          M        8                       OT
  NLP04   11.0          M        0                       ROP
  NLP05   13.8          F        1                       ROP; EA
  NLP06   17.9          F        5                       Cataract; EA
  NLP07   12.3          F        3                       ROP
  NLP08   17.1          F        0                       ROP; OA
  NLP09   17.8          M        0                       ROP
  NLP10   14.5          F        1                       ROP
  NLP11   14.8          M        4                       ROP
  NLP12   14.9          M        0                       CRL
  NLP13   11.1          F        3                       ET; EE
  NLP14   13.2          M        0                       CRL
  NLP15   13.7          F        0                       ROP
  NSC01   12.5          F        ---                     ---
  NSC02   12.0          M        ---                     ---
  NSC03   14.0          M        ---                     ---
  NSC04   17.2          M        ---                     ---
  NSC05   13.6          M        ---                     ---
  NSC06   12.5          M        ---                     ---
  NSC07   13.3          M        ---                     ---
  NSC08   18.3          F        ---                     ---
  NSC09   12.1          M        ---                     ---
  NSC10   18.2          F        ---                     ---
  NSC11   15.5          M        ---                     ---
  NSC12   16.5          M        ---                     ---
  NSC13   13.9          M        ---                     ---
  NSC14   18.7          F        ---                     ---
  NSC15   17.6          F        ---                     ---
  NSC16   17.0          F        ---                     ---
  NSC17   15.4          F        ---                     ---
  NSC18   15.0          F        ---                     ---
  NSC19   11.0          F        ---                     ---
  NSC20   15.0          F        ---                     ---
  NSC21   13.0          F        ---                     ---

Abbreviations: LP: early-blind adolescents with light perception; NLP: early-blind adolescents without light perception; NSC: normal-sighted controls; M: male; F: female; ROP: retinopathy of prematurity; OT: oxygen toxicity; EA: eyeball atrophy; CRL: congenital retinal lesions: CG, congenital glaucoma; OA: optic atrophy; ET: eyeball tumor; and EE: eyeball extraction.

###### 

Demographic characteristics of the recruited subjects.

                 LP           NLP          NSC          ANOVA statistics   *p* value
  -------------- ------------ ------------ ------------ ------------------ -----------
  Gender (M/F)   6/2          8/7          10/11        *F* = 0.857        0.432
  Age (years)    13.2 ± 1.5   14.7 ± 2.3   14.9 ± 2.3   *F* = 1.782        0.181

Abbreviations: LP: early-blind adolescents with light perception; NLP: early-blind adolescents without light perception; NSC: normal-sighted controls; ANOVA: analysis of variance; M: male; F: female.

###### 

Brain regions with significant changes in diffusion tensor imaging metrics.

  Between-group comparison                         DTI parameter changes   Localizations of peak voxels   Abbreviations   Cluster size (voxels)   *T* score   Peak MNI         
  ------------------------------------------------ ----------------------- ------------------------------ --------------- ----------------------- ----------- ---------- ----- -----
  NLP vs. NSC (*p* \< 0.001, AlphaSim corrected)   FA reduction            Left occipital lobe/subgyral   OG.L            5196                    -7.73       -32        -62   -2
  Right parahippocampal                            ParaHIP.R               978                            -10.06          12                      2           -22              
  Right superior occipital gyrus                   SOG.R                   255                            -8.34           24                      -92         32               
  MD elevation                                     Left lingual            LING.L                         3698            5.85                    -28         -54        -4    
  Right lingual                                    LING.R                  3596                           5.84            20                      -54         2                
  RD elevation                                     Right lingual           LING.R                         4030            6.74                    14          -80        0     
  Right parahippocampal                            ParaHIP.R               7113                           7.07            10                      4           -18              
  LP vs. NSC (*p* \< 0.001, AlphaSim corrected)    FA reduction            Left parahippocampal           ParaHIP.L       319                     -6.58       -12        2     -20
  Left middle occipital gyrus                      MOG.L                   198                            -5.98           -26                     -100        2                
  Left occipital lobe/subgyral                     OG.L                    224                            -5.71           -32                     -68         -2               
  Left lingual                                     LING.L                  142                            -5.11           -4                      -74         -4               
  Right middle occipital gyrus                     MOG.R                   62                             -5.99           38                      -70         0                
  Left cuneus                                      CUN.L                   154                            -5.98           -8                      -84         30               
  Right parietal lobe/subgyral                     PG.R                    120                            -6.65           32                      -40         24               

Abbreviations: LP: early-blind adolescents with residual light perception; NLP: early-blind adolescents without light perception; NSC: normal-sighted controls; FA: fractional anisotropy; MD: mean diffusivity; RD: radial diffusivity; and MNI: Montreal Neurological Institute.

###### 

Brain regions with significant changes of RSFC between groups.

  Between-group comparison                       ROI seeds                     Brain regions                                  Abbreviations   Cluster size (voxels)   *T* score   Peak MNI         
  ---------------------------------------------- ----------------------------- ---------------------------------------------- --------------- ----------------------- ----------- ---------- ----- ---
  NLPs vs. NSC                                   OG.L                          Left inferior frontal gyrus, triangular part   IFGtri.L        77                      5.12        -39        33    6
  Right inferior frontal gyrus, opercular part   IFGoper.R                     115                                            6.77            57                      21          33               
  Left middle frontal gyrus                      MFG.L                         97                                             5.68            -45                     18          18               
  ParaHIP.R                                      Left superior frontal gyrus   SFG.L                                          67              5.96                    67          -24        30    
  SOG.R                                          Left fusiform gyrus           FG.L                                           188             -5.93                   -24         -66        -15   
  Right supramarginal gyrus                      SMG.R                         62                                             5.16            60                      -33         39               
  Left superior frontal gyrus                    SFG.L                         48                                             4.63            24                      -93         6                
  LPs vs. NSC                                    MOG.L                         Right middle occipital gyrus                   MOG.R           47                      -5.15       24         -93   6

The results were corrected by the AlphaSim method at a voxel level of *p* \< 0.001. Abbreviations: RSFC: resting-state functional connectivity; ROI: region of interest; OG.L: left occipital gyrus; ParaHIP.R: right parahippocampal gyrus; SOG.R: right superior occipital gyrus; MOG.L; left middle occipital gyrus; LP: early-blind adolescents with residual light perception; NLP; early-blind adolescents without light perception; NSC: normal-sighted controls; MNI: Montreal Neurological Institute.

[^1]: Academic Editor: Andrea Guzzetta