Article The Infuence of Intraocular Lens Implantation and Alterations in Blue LightTransmittance Level on the Brain Functional Network Architecture Reorganization in Cataract Patients Anna Maria Sobczak 1,2,*, Bartosz Bohaterewicz 1,3,* , Magdalena Fafrowicz 1,2 , Aleksandra Domagalik 2 , Ewa Beldzik 1,2 , Halszka Oginska 1,2, Natalia Golonka 1, Marek Rekas 4, Dominik Bronicki 4, Bo˙, FarzadV. Farahani 6,7 zena Romanowska-Dixon 5, Joanna Bolsega-Pacud 5,Waldemar Karwowski 6 and Tadeusz Marek1,2 Citation: Sobczak, A.M.; Bohaterewicz, B.; Fafrowicz, M.; Domagalik, A.; Beldzik, E.; Oginska, H.; Golonka, N.; Rekas, M.; Bronicki, D.; Romanowska-Dixon, B.; et al. The Infuence of Intraocular Lens Implantation and Alterations in Blue LightTransmittance Level on the Brain Functional Network Architecture Reorganization in Cataract Patients. Brain Sci. 2021, 11, 1400. https://doi.org/10.3390/ brainsci11111400 Academic Editor:WernerM. Graf Received: 21 July 2021 Accepted: 19 October 2021 Published: 24 October 2021 Publisher’s Note: MDPI stays neutral with regardto jurisdictional claims in published maps and institutional affliations. 1 Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, 30-348 Kraków, Poland; vonfrovitz@gmail.com (M.F.); ewa.beldzik@gmail.com (E.B.); halszka.oginska@gmail.com (H.O.); n.a.golonka@gmail.com (N.G.); tademarek@gmail.com (T.M.) 2 Malopolska Centre of Biotechnology, Jagiellonian University, 30-387 Kraków, Poland; aleksandra.domagalik@uj.edu.pl 3 Department of Psychology of Individual Differences, Psychological Diagnosis, and Psychometrics, Instituteof Psychology, Universityof Social Sciences and Humanities, 03-815Warsaw, Poland 4 Ophthalmology Department, Military Instituteof Medicine, 04-349Warsaw, Poland; mrekas@wim.mil.pl (M.R.); d.bronicki@gmail.com (D.B.) 5 Department of Ophthalmology and Ocular Oncology, Medical College, Jagiellonian University, 31-008 Kraków, Poland; bozena.romanowska-dixon@uj.edu.pl (B.R.-D.); joanna.bolsega@gmail.com (J.B.-P.) 6 Computational Neuroergonomics Laboratory, Departmentof Industrial Engineering&Management Systems, Universityof Central Florida, Orlando,FL 32816, USA; wkar@ucf.edu(W.K.); farzad.vasheghani@knights.ucf.edu (F.V.F.) 7 Biostatistics Department, John Hopkins University, Baltimore, MD 21218, USA * Correspondence: ann.marie.sobczak@gmail.com (A.M.S.); bohaterewicz@gmail.com (B.B.) Abstract: Background: Cataract is one of the most common age-related vision deteriorations, leading to opacifcation of the lens and therefore visual impairment as well as blindness. Both cataract extraction and the implantation of blue light fltering lens are believed to improve not only vision but also overall functioning. Methods: Thirty-four cataract patients were subject to resting-state functional magneticresonance imaging before and after cataract extraction and intraocular lens implantation (IOL). Global and local graph metrics were calculated in order to investigate the reorganization of functional network architecture associated with alterations in blue light transmittance. Psychomotor vigilance task (PVT) was conducted. Results: Graph theory-based analysisrevealed decreased eigenvector centrality afterthe cataract extractionandIOLreplacementin inferior occipitalgyrus, superior parietalgyrusandmanycerebellumregionsaswellasincreased clusteringcoeffcientin superiorand inferior parietal gyrus, middle temporal gyrus and various cerebellum regions. PVT results revealed signifcant change between experimental sessions as patients responded faster after IOL replacement. Moreover, a few regions were correlated with the difference in blue light transmittance and the time reaction in PVT. Conclusion: Current study revealed substantial functional network architecture reorganization associated with cataract extraction and alteration in blue light transmittance. Keywords: neuroimaging; fMRI; graphs; cataract; blue light Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1. Introduction Ageing entails numerous changes affecting the brain and eyes, which are strongly connected with each other functionally and anatomically[1,2]. Cataractis oneof the most common age-related vision deteriorations, leading to opacifcation of the lens and therefore visual impairmentand blindness. Accordingto Pascoliniand Mariotti’s[3]reportforthe World Health Organization, cataract is the second (33%) cause of visual impairment and Brain Sci. 2021, 11, 1400. https://doi.org/10.3390/brainsci11111400 https://www.mdpi.com/journal/brainsci the frst cause of blindness (51%). Age-related cataract constitutes the largest group of cataract patients and according to some studies, it may be a manifestation of an extensive degenerative process containing extended brain damage and the subsequent disturbed behavior[4,5].Duetothe accumulationofchromophoresthe naturallens becomemore yellow during ageing[6]and, therefore, flter the blue lightreaching theretina. This particular part of the spectrum is crucial for entrainment of the circadian system and has signifcanteffectson alertingand cognitiveresponses[7]. Cataract extraction (cataract surgery) is a surgical procedure involving the removal of a natural lens and implanting the intraocularone(IOL)withabluelight flteror withoutit (crystallens). Accordingtothe reviewbyDavidsonetal.[8], patientswithbluelight-flteringIOLs “should experience the beneft of overall better quality of vision, reduced glare disability at least in some conditions, and better protection against retinal phototoxicity”. However, some previous studies have shown that in a healthy ageing brain the effect of blue light diminishes, particularly in the areas responsible for vision, alertness regulation and higher executive processes[9]. Nevertheless,previous studies havereported positive impactof cataract surgery[10,11].For example,Linetal.[11]observedincreasedgrey matter volumeofthe visual, cognitive-related, and somatosensory brain areas. The above observations may suggest such improvement in brain functioning as vision-related quality of life, cognitive impairment and depressive state, which are believed to be strongly connected with each other[10]. Normalizing vision after cataract surgery was also suggested to have impact on memory and learning[12]. Apart from that, cataract extraction may also improve cognitive function, but it is still unclear whether it is a matter of improved visual function or cataract surgeryassuch[13,14]. Essentially, cataract extraction beneftsare believedtobe extended beyond the visual acuity, as it provides improvements in cognition, emotions and general well-being[15]. Vision impairments affect the overall nervous system functioning, which can be investigated with the use of resting-state fMRI. Graph-theory based analysis is one of the well-known methods to evaluate the global and local functional reorganization of the neuronal network.Oneofthebiggest advantagesofconductinggraph analysisonfMRI data is the ability to investigate intermediate and high levels of organization across the networkasa whole[16]. Thusfar,graphtheoryhasbeenusedto quantify abnormality of structural and functional networks in various disorders such as borderline personality disorder[17], autism spectrum disorder[18], social anxiety[19]or even illnesses like cancer[20]and diabetes[21]. The current study is a follow up to our previous work investigating hemodynamic bases of daytime sleepiness, experiencing pleasure as well as positive and negative affect in cataract patients[22]. The purposeof ourresearchistostudythe consequencesof cataract extraction and changein the amountof blue light thatreaches theretina after this intervention on the brain functional network architecturereorganization.We hypothesize that brain activity of the aforementioned participants will vary on a global and local level before and after the implantation of the lens and that it will be associated with the changeinbluelight transmittance.We assume overall betterorganizationofthe functional neural networks after the cataract extraction, IOL implantation and alterations in blue light transmittance. Above results would mean higher integration, synchronization, robustness as well as effectiveness of the functional networks after IOL implantation. We strongly believe the aforementioned analyses can enrich the current knowledge regarding the mechanisms of cognitive defcits associated with vision impairments. 2. Materials and Methods 2.1. Participants Ophthalmologists recruited 38 healthy patients with diagnosed cataract, however four of them had to drop out after the frst session due to health issues. As a result, a total of 34 participants were subject to fMRI examination before and after cataract extraction and the implantation of an intraocular lens. The mean age of participants was M=62.3yearsold,SD =9.1(range34–74yearsold),andthesample consistedof22 women and12men.The participantsweresubjecttofMRI examination2weeksbeforethesurgery and 6–12 months after (M = 9.22; SD = 2.66), due to individual recovery process as well as the fact that the patients were localized in the different parts of the country. The patients were recruited by qualifed ophthalmologists after being diagnosed withthe cataract in the Polish national healthcare system. The inclusion criteria were being diagnosed with a cataract as well as the qualifcation for the surgery. The exclusion criteria were psychiatric and neurological disorders, lesions and contraindications for magnetic resonance imaging. Thestudywasapprovedbythe Bioethics CommissionatthePolishMilitary InstituteofAviation Medicine,Warsaw, Poland(report number 02/2013;26 February 2013) and Institute of Applied Psychology atthe Jagiellonian University, Cracow, Poland (21 February 2017). It was also conducted in accordance with ethical standards described in the Declaration of Helsinki. Moreover, all participants were informed about the procedure and provided their written consent. 2.2. IOL and Blue Light Transmittance Before IOL implantation, patients had their lens measured yellowing either with the use of fuorophotometry (Ocumetrics Fluorotron Master) or with the Lens Opacities Classifcation System III. (LOCS III).Transmittance data were obtained directlyfrom the frst method and were calculated based on Siik et al.[23]for the LOCS III measurements. There were no data for four patients, thus transmittance level was estimated based on the existing data and age. Ophthalmologists recruited patients who had surgery under the national health care. The IOL type was also chosenby ophthalmologists. Patients had two types of IOL implanted: with blue light flter transmitting 68% of light around 475 nm (Alcon AcrySof® IQ model SN60WF) or so-called crystal lenses transmitting 95% of blue light (HOYALtd. model iSert 250, Abbott Medical Optics Inc. model Symphony, Alcon AcrySof® IQ model AU00T0, Akreos® model Adapt AO). The difference between blue light transmittance before and after IOL implantation was calculated for each patient. The eyes with higher transmittance before and after the intervention were taken for this calculation (see Supplementary Materials for raw data). 2.3. Behavioral Measurements At each session, before fMRI scanning participants performed a psychomotor vigilance task (PVT), a widely used test of sustained attention measuring the speed with which subjectsrespondtoa visualstimulus[24,25].Task was performedona computer with a 19-inch LCD screen and responses were made with arrow or space keys on the keyboard. Participantswereinstructedtopressabuttonwithindexfngerassoonasthe stimulus appears, which stops the stimulus counter and displays the reaction time (RT) in milliseconds for a 1s period. It was emphasized not to press the button in the absence of stimuli, (in such case a false start warning appeared on the screen). If a reaction was slower than1s, the warning “too slow” was presented. The inter-trial interval varied randomlyfrom2to10s,andthetask durationwas5min, comprisingabout42 stimuli. For correctresponses meanRT, meanRTfor 10%of the fastestresponses and meanRTfor 10%ofthe slowestresponseswere calculatedasthemostfrequentlyreportedPVT outcome metrics[24]. The frst three stimuli were discardedfrom the analysisin each PVTtrial.A t-test was performed to comparethe outcomes between the session before and after the IOL replacement. In order to check whether the difference in performance between sessions is related to change in the blue light transmittance, the correlation analysis was conducted. 2.4. MRI Data Acquisition MRI data were acquired using 3T Siemens Skyra MR System (Siemens Medical Solutions, Erlangen, Germany). Structural images were obtained using sagittal 3D T1-weighted MPRAGE sequence. Total of 10 min functional resting state (rs-fmri) EPI images were acquired using gradient-echo single-shot echo planar imaging sequence with the following parameters:TR =2000ms;TE =27ms;slice thickness =3mm,voxelsize=3mm3,withno gap using 20-channel coil.Totalof37 interleaved transverse slices and 300 volumes were acquired. During the acquisition, participants were instructed to keep their eyes open and to not think about anything in particular. 2.5. Imaging Data Preprocessing The rs-fMRI data processing was performed using Data Processing&Analysis for Brain Imaging(DPABI) V4.3[26]and SPM12(WellcomeTrust Centre for Neuroimaging, UCL, London, UK) both working under MATLAB version R2018a (The MathWorks, Inc., Natick, MA, USA). First 10 time points were discarded due to signal equilibration and then slice timing was conducted. Moreover, realignment withassessment of the voxel specifc headmotionwas conducted.Noneoftheparticipantsdisplayed movementsabove3mmor 3◦ in one or moreof the orthogonal directions and thereforeall patients qualifed for further analysis. Then, using standardEPI template functional images were linearly normalized in DARTEL to Montreal Neurological Institute (MNI) space and spatially resampled to 3× 3× 3mm voxel size. The 24 motion parameters derived from the realignment step, white matter as well as cerebrospinal fuid signals and fve principal components were removed using principal components analysis integrated in a Component Based Noise Correction Method[27]. The global signal was included due to its potential to provide additional valuable information[28]. The signal was then band-pass fltered (0.01–0.08 Hz) toreduce high-frequency noise and low-frequency drift, such as therespiratory and cardiac rhythms. Finally, the functional data were spatially smoothed with 4mm FullWidth at Half Maximum (FWHM) kernel. 2.6. Parcellation The preprocessed data were parcellated using Automated Anatomical Labeling (AAL) atlas which separates the brain into 116regions[29]. In order to investigate possible between-session differences among Default Mode Network, Salience Network, Basal gan-glia Network as well as HigherVisual Network, PrimaryVisual Network andVisuospatial Network, the authors used templates from FIND lab(http://fndlab.stanford.edu/ functional_ROIs.html, accessed on 15 November 2020). 2.7. Graph Metrics In order to examine the topological properties of functional brain network for each participant at both global and local levels Graphvar 2.02b and MATLAB version R2018a (The MathWorks, Inc., Natick, MA, USA) were used. Global measures aimed at describing macroscale organization and integration of all nodes in the brain network and included: mean clustering coeffcient and assortativity. Local properties were calculated for each individualnode(region) separately,refectingthe nodal centralityinthe network.Inthis study, we calculated common local properties such as clustering coeffcient and eigenvector centrality (the measures are discussed in detail in https://sites.google.com/site/bctnet/ measures/list, accessed on 15 November 2020). Data used for graph measures were not smoothed during preprocessing steps. For each subject, 116 regions of interest (ROIs) were defned accordingtotheAALatlas[29].Inorderto obtaina116 × 116 undirected binary correlation matrix, mean time course for each region was extracted and then the Pearson coeffcients between each pair of ROIs were calculated. In order to exclude the spurious linksin interregional connectivity matrices[30], we adopteda thresholdingprocedure basedonthestrongest connections,removingthe weaker ones[31]. Thisprocedure enables to compare network topology within as well as between participants[32]. Network edges were defned using a sparsity thresholding procedure ranging from 0.1 to 0.5 in steps of 0.05. 2.8. Statistical Analysis Paired t-test was used in order to comparegraph indexes beforeand after implantation of an intraocular blue light flter lens. All the results were calculated with 5000 iterations and corrected with the Benjamini and Hochberg[33]False Discovery Rate correction at p <0.05. Paired t-test with 5000 iterations as well as non-parametric FDR corrected p-value<0.05 was conducted using Graphvar 2.02b andMATLAB version R2018a (The MathWorks, Inc., Natick, MA, USA). Pearson correlation was calculated in order to in vestigate the association betweengraphresults and both blue light transmittance and PVT results. 3. Results All PVT outcomes showed signifcant change between experimental sessions, i.e., patientsresponded faster after IOLreplacement: meanRT(p = 0.001,t = 3.65), medianRT (p = 0.002,t = 3.44) andRTof 10% fastest(p = 0.004,t = 3.25) but not the slowest(p = 0.072, t= 1.88)responses. Results arepresented on Figure 1. The correlation analysis between difference in PVT outcomes between sessions and the difference in blue light transmittance did notreveal signifcantresults(p >0.3). Figure 1. Boxplot showing reaction time distribution before and after Intraocular lens implantation. * Indicate signifcant difference between sessions. In case of resting-state fMRI analyses, paired t-test revealed signifcant, FDR corrected (p < 0.05) differences in eigenvector centrality values. Among others, the patients manifested higher eigenvector centralityofVermis8before cataract extraction and intraocular lens implantation. Moreover, nodes such as bilateral inferior occipital,bilateral superior parietal, right supramarginal as well as various cerebellum regions turned out to be signifcantly more important for the whole network before the intraocular lens implantation. Moreover,bilateral supplementary motor areapresenteda signifcantly lower tendency for clustering in networks after the surgery, while right superior parietal, left inferior parietal, bilateral middle temporal pole and cerebellum regions clustered to a greater extent. All the resultsfrom graph analyses arepresentedinTable 1. The correlation analysisof all signifcant graphresults with the differencein blue light transmittancerevealed signifcant positive correlation on two thresholds between eigenvector centrality in right cerebellum 7b and the difference in the level of the exposure to the blue light(threshold = 0.1, r = 0.37, p = 0.032; threshold = 0.3, r = 0.35, puncorrected = 0.043). Correlation analysisof signifcant graphresults with the PVTresultsrevealed signifcant negative correlation betweenthedifferenceinthereaction timeand clustering coeffcientin the right cerebellum 7b in threshold 0.35 (r = −0.34; puncorrected = 0.04), the same structure which was positively correlated with the differencein blue light transmittance. Aforementioned correlations with cerebellum7b arepresentedin Figure 2. Moreover, differencein PVT results were negatively correlated with the eigenvector centrality in the right inferior occipital gyrus in two thresholds: 0.3 (r = −0.4; puncorrected = 0.017) and 0.4(r = −0.36; puncorrected = 0.03). The strongest correlation of altered eigenvector centrality and difference in PVTis visualizedin Figure 3. Table 1. List of brain ROIs with clustering coeffcient and eigenvector centrality values before and after the intraocular lens implantation. p-Value ROI (Names) AAL Label Threshold Clustering Coeffcient Eigenvector Centrality Preoperative-Postoperative Left Supplementary Motor Area SMA.L 0.2 0.035 Right Supplementary Motor Area SMA.R 0.4 0.027 Left Inferior Occipital Gyrus IOG.L 0.4 0.034 0.3 0.009 Right Inferior Occipital Gyrus IOG.R 0.4 0.02 0.5 0.015 0.15 0.042 Left Superior Parietal Gyrus SPG.L 0.2 0.017 0.3 0.038 Right Superior Parietal Gyrus SPG.R 0.15 0.016 0.15 0.004 Right Supramarginal Gyrus SMG.R 0.35 0.029 0.2 0.046 0.3 0.019 Left CerebellumCrus2 CRBLCrus2.L 0.4 0.014 0.5 0.016 Left Cerebellum 7b CER7b.L 0.35 0.024 0.1 0.018 0.15 0.02 Right Cerebellum 7b CER7b.R 0.2 0.017 0.25 0.027 0.3 0.04 Left Cerebellum 8 CER8.L 0.1 0.03 Right Cerebellum 8 CER8.R 0.15 0.048 0.45 0.03 Left Cerebellum 9 CER9.L 0.45 0.006 Right Cerebellum 9 CER9.R 0.25 0.011 Vermis 8 VER8 0.1 0.0002 Postoperative-Preoperative 0.45 0.027 Right Superior Parietal Gyrus SPG.R 0.5 0.046 0.2 0.022 0.25 0.021 Left Inferior Parietal Gyrus IPL.L 0.35 0.014 0.4 0.014 Table 1.Cont. p-Value ROI (Names) AAL Label Threshold Clustering Coeffcient Eigenvector Centrality 0.15 0.016 0.2 0.015 Left CerebellumCrus2 CRBLCrus2.L 0.25 0.048 0.3 0.016 0.1 0.022 Right Cerebellum Crus 2 CRBLCrus2.R 0.25 0.015 Right Cerebellum 7b CER7b.R 0.35 0.049 Left Cerebellum 8 CER8.L 0.15 0.048 Left Cerebellum 10 CER10.L 0.5 0.01 0.2 0.033 0.25 0.034 Vermis8 VER8 0.3 0.023 0.35 0.025 0.4 0.022 Figure 2. (a)Right Cerebelum_7b (aal) ROIrendered on cerebellum surface;(b)lineplot with Eigenvector centrality raw values andabsolute differencein transmittanceof each participant;(c)Right Cerebelum_7b (aal) ROI rendered on the cortical surface;(d)lineplot with Clustering coeffcientraw values and the differencein PVTof each participant; values for each index were z-score for the purpose of visualization. inferior occipital gyrus. Figure 3. Scatter plot showing the relationship between difference in the reaction time and eigenvector centrality in right 4. Discussion The current study sought to investigate global and local functional network architecture reorganization of the brain associated with cataract extraction and the difference in blue light transmittance. The proposed local graph metrics revealed the substantial reorganization of functional network architecture, indicating the increase in the clustering coeffcient of superior and inferior parietal, middle temporal as well as various cerebellar regions. Large clustering coeffcient is reported to be characteristic for a small-world network[34]andcreating small worldsin neuronal networksis thoughttobeaproperty of healthy brain as the loss of small-worldness is a well-known signature of Alzheimer’s disease[35]and schizophrenia[36]. Moreover, larger clustering coeffcient allows differentiating the healthy participants from the ones with an early onset of neurodegenerative dementia[37].In addition, Masudaetal.[38]revealed signifcant declineinthe clustering coeffcient associated with age. The aforementioned reports are congruent with our results, showing considerable increase in the local clustering coeffcient after cataract extraction and intraocular lens implantation primarily among the elderly patients. It indicates higher integration and better functioning of brain networks. Larger clustering coeffcient in inferior and superior parietal gyrus is probably associated with functional recovery as well as visualrestoration, which was already establishedinthe studyof Linet al.[11]. The authors proved that after the cataract surgery, both visual and cognitive functions can be not only enhanced but even fully reversed to the normal level of functioning. Our other results from the bilateral middle temporal poles also stay in line with theliterature as this specifcregionis believedtoplaya signifcantrolein conceptualprocessingof visual objects[39]. However, apparently visualimprovementisnottheonlypronouncedeffect associated with cataract extraction and intraocular lens implantation. The enlarged clusteringcoeffcientinthecerebellum7b,8a,Crus2 aswellas vermis8indicates considerable functional reorganization associated with motor and cognition alterations. For instance, cerebellumcrus2is, among others,relatedto emotional cognition[40]whichisreported to boost after cataract extraction[15].Vermis8,in turn,isresponsible for bodily posture and locomotion, consideredtobe impaired among people with visual defcits[41,42].In addition, the current study provides results proving cognitive enhancement caused by cataract surgery. The analysis of local graph metrics revealed larger clustering coeffcient in the cerebellum 7b and 8a, which are strongly involved in visual working memory and visual attention tasks.Moreover,thestudyof Brissendenetal.[43]showsthattheabove cerebellum areas manifest intrinsic functional connectivity with dorsal attention network. Noteworthy, clustering coeffcient in cerebellum 7b turned out to be negatively correlated with the difference in the results of PVT, a widely known test of the sustained attention, which confrms association of cerebellum 7b with the attention network. Concluding, the increased integration of aforementioned regions proves not only visual but also motor and cognitive-related improvement associated with cataract extraction as well as intraocular lens implantation. Alteration in eigenvector centrality is another proof for signifcant functional network architecturereorganization. Eigenvector centralityisa self-referential measureof centrality. Nodes with high eigenvector centrality are connected to other nodes with high eigenvector centrality. It means that the node is important for the network but at the same time is connected to other nodes which are very important for the network. The local graph measure allowed identifying signifcant changes in prominent regions in the hierarchy of brain networks. Eigenvector centrality of the neuron is thought to positively correlate withitsrelativefringrate[44].Accordingtothespikingneural networkmodel,increased fring rate constitutesa compensatory mechanism whichprevents the disruptionof neural network homeostasis afterprogressive lossof synapses[45,46]. The aboveresults are congruent with the previous study reporting higher eigenvector centrality in participants with longer alcohol dependence[47]. The current studyrevealed increased eigenvector centrality in bilateral inferior occipital gyrus, bilateral superior parietal gyrus as well as various cerebellum regions in cataract patients before cataract extraction and intraocular lens implantation. The above results may contribute to a spiking neural network model, as both occipital and parietal regions are believed to present impaired functioning in cataract patients before the surgery[11]. Moreover, mostof the cerebellumregions with increased centrality beforethe surgery arethe same ones which showed increased clustering coeffcient and thereby higher integration after the cataract extraction. The association between altered eigenvector centrality and functional reorganization patterns are still not fully understood and should be further investigated. In addition, eigenvector centrality in cerebellum 7b turned out to be positively correlated with the difference in the level of blue light transmittance. The above region is thoughttobe associatedwith non-motorrepresentationsinthe brain[48].Theprevious study, conductedon228 healthy subjectsrevealedstrongresting-state functional connectivity betweencerebellum7band saliencenetwork[49], whichisreportedtoberesponsible for detectingrelevant stimuli as well as coordinating therespective brainresponse[50]. Moreover, Brissendenetal.[43]reportedthatthe samecerebellumregionis functionally connected with the dorsal attention network. The previous results prove cerebellum 7b is related to attention and processing sensory-motor information. Thereby, the current research is congruent with other previous studies pointing to the association between blue light exposure and alertness for external stimuli as well as overall attention[51,52]. Summarizing, blue light transmittance proves to be related to functional reorganization incerebellum7b,theregionstrongly associatedwithstructures whichplaya signifcant role in attention and integration of external stimuli. Interestingly, eigenvector centrality in the right inferior occipital gyrus after the IOL implantation turned out to be negatively correlatedwiththedifferenceinreactiontimein psychomotor vigilance test. Aboverelation means that the lower eigenvector centrality after the cataract extraction, the faster patients reacted to the salient stimuli on the screen after the surgery. Aforementioned results stay in line with the literature considering better visual and attentional performance to be a consequence of cataract extraction[53]. Importantly, improvement of visual acuity is the most reported outcome after the cataract extraction, there are studies showing that the procedure can signifcantly improve brain functions as a function of increased gray matter volume in brain areas related with cognitive and visual functions[11]. 5. Limitation The current study has the restricted sample size and further study should consider extending it. Secondly, the sample size is not equivalent in the case of gender and the age range could be smaller. Moreover, fMRI studies struggle with the low time resolution, however our time repetition had been established to 1000 TR, hence the limitation is minimalized. In addition, both local and global graph metrics have been chosen according to the best authors knowledge, however the process of selection was still arbitrary because there are a lot of other metrics and none of them is believed to be the best index for describing neuralreorganization. Finally,futurestudies should collect moredemographical data as well as cognitive measurements. 6. Conclusions Our results reveal tremendous functional network architecture reorganization of neuronal networks caused by cataract extraction, intraocular lens implantation as well as altered blue light transmittance. To the best of our knowledge this is the frst report addressing this problem, based on graph theory and conducted with the use of resting state fMRI data. Supplementary Materials: The following are available online at https://www.mdpi.com/article/10 .3390/brainsci11111400/s1,Table S1: Levelof blue light transmittancebefore and after cataract ex
traction. Author Contributions: M.F.,T.M.,A.D.,E.B.,H.O.,M.R., B.R.-D.andW.K.: conceptionanddesignof the work. M.F.: data collection supervision. M.F., A.D., D.B. and J.B.-P.: data acquisition. A.M.S., B.B. andF.V.F.: rs-fMRI data analysis. A.M.S., B.B., M.F.,T.M. and A.D.: interpretationof theresults. M.R. and B.R.-D.: cataract patients’ diagnosis and I.O.L. implantation supervision. A.M.S., B.B., A.D., M.F., T.M. and N.G.: drafting the work.A.M.S., B.B., M.F.,T.M., A.D., E.B., H.O., M.R., B.R.-D., D.B., J.B.-P., W.K.andF.V.F.:revisingthe manuscript criticallyandfnalapprovalofthe versiontobe published. All authors have read and agreed to the published version of the manuscript. Funding: ThisstudywasfundedbythePolishNationalScienceCentrethroughSymfonia(2013/08/W/ NZ3/00700) and partly supportedby the Foundation for Polish Science (FNP)project “Bio-inspired Artifcial Neural Networks” (POIR.04.04.00-00-14DE/18-00). Institutional Review Board Statement: The study was reviewed and approved by the Bioethics Commission at the Polish Military Institute of Aviation Medicine (report number 02/2013; 26 February 2013) and Institute of Applied Psychology at the Jagiellonian University, Cracow, Poland (21 February 2017). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. DataAvailability Statement: The data can be shared upon the request. Acknowledgments: We thank MagdalenaWielgus and Aleksandra ˙ Zyrkowska for support in data acquisition.In addition,wewouldliketothankPiotrFabaforhis technical supportonthisproject and help in data acquisition. Conficts of Interest: The authors declare no confict of interest. References 1. Schnitzer, M.J.; Meister, M. Multineuronal Firing Patterns in the Signal from Eye to Brain. Neuron 2003, 37, 499–511.[CrossRef] 2. Chen,C.;Bickford,M.E.; Hirsch,J.A. UntanglingtheWeb betweenEyeand Brain. 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